From b9ba854b5cee57f58e46bea8b232518d850dfc5c Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 11:55:53 +0530
Subject: [PATCH 01/32] Add files via upload

---
 Prediction using Supervised ML/Task_01.ipynb | 1046 ++++++++++++++++++
 1 file changed, 1046 insertions(+)
 create mode 100644 Prediction using Supervised ML/Task_01.ipynb

diff --git a/Prediction using Supervised ML/Task_01.ipynb b/Prediction using Supervised ML/Task_01.ipynb
new file mode 100644
index 0000000..2de89af
--- /dev/null
+++ b/Prediction using Supervised ML/Task_01.ipynb	
@@ -0,0 +1,1046 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Task_01.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z1dOavLaFTCn"
+      },
+      "source": [
+        "# Author : Vedanti Ekre\n",
+        "\n",
+        "#Email: vedantiekre@gmail.com\n",
+        "\n",
+        "## Task 1 : Prediction using Supervised Machine Learning\n",
+        "___\n",
+        "## GRIP @ The Sparks Foundation\n",
+        "____\n",
+        "# Role : Data Science and Business Analytics [Batch May-2021]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6EY1YO2Z9tga"
+      },
+      "source": [
+        "## TABLE OF CONTENTS:\n",
+        "\n",
+        "1. [Introduction](#intro)\n",
+        "2. [Importing the dependencies](#libs)\n",
+        "3. [Loading the Data](#DL)\n",
+        "4. [Understanding data](#UD)\n",
+        "5. [Spliting data in Test and Train](#split)\n",
+        "6. [Use Smiple Linear Regression Model to do prediction](#LR)\n",
+        "7. [Task](#PT)\n",
+        "8. [Evaluate the model using MAE and MSE metrics](#Eval)\n",
+        "9. [Conslusion](#conclu)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DX7H6U-tPXx6"
+      },
+      "source": [
+        "## **Introduction**<a class=\"anchor\" id=\"intro\"></a>\n",
+        "● We have given Student dataset,which have only two features Hours and scores.<br>\n",
+        "● Predict the percentage of an student based on the no. of study hours.<br>\n",
+        "● This is a simple linear regression task as it involves just 2 variables.<br>\n",
+        "● You can use R, Python, SAS Enterprise Miner or any other tool<br>\n",
+        "● Data can be found at http://bit.ly/w-data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fPIIqDQCFTCq"
+      },
+      "source": [
+        "## Importing dependencies<a class=\"anchor\" id=\"libs\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2lveKmLX7U_1"
+      },
+      "source": [
+        "#importing packages\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PWkEOHEj-Ar1"
+      },
+      "source": [
+        "## **Loading the Data**<a class=\"anchor\" id=\"DL\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GX_rdxGU75EK"
+      },
+      "source": [
+        "#importing datasets\n",
+        "url = \"http://bit.ly/w-data\"\n",
+        "data = pd.read_csv(url)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1bJoiktf-Gmb"
+      },
+      "source": [
+        "## **Understanding data**<a class=\"anchor\" id=\"UD\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 205
+        },
+        "id": "OdreYCnA-H9G",
+        "outputId": "294cc237-5efc-469c-df7a-a8229376d730"
+      },
+      "source": [
+        "display(data.head(3),data.tail(3))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Hours</th>\n",
+              "      <th>Scores</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.5</td>\n",
+              "      <td>21</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>47</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.2</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Hours  Scores\n",
+              "0    2.5      21\n",
+              "1    5.1      47\n",
+              "2    3.2      27"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Hours</th>\n",
+              "      <th>Scores</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>3.8</td>\n",
+              "      <td>35</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>6.9</td>\n",
+              "      <td>76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>86</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Hours  Scores\n",
+              "22    3.8      35\n",
+              "23    6.9      76\n",
+              "24    7.8      86"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xgy95nk1-KBF",
+        "outputId": "533b0c5c-574e-483f-b6ad-f8b4d26bc1c6"
+      },
+      "source": [
+        "print(type(data))\n",
+        "print('-'*45)\n",
+        "print('The data set has {} rows and {} columns'.format(data.shape[0],data.shape[1]))\n",
+        "print('-'*45)\n",
+        "print('Data types :') \n",
+        "print(data.dtypes.value_counts())\n",
+        "#print('Total : ',data.dtypes.value_counts().sum())\n",
+        "print('-'*45)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "---------------------------------------------\n",
+            "The data set has 25 rows and 2 columns\n",
+            "---------------------------------------------\n",
+            "Data types :\n",
+            "int64      1\n",
+            "float64    1\n",
+            "dtype: int64\n",
+            "---------------------------------------------\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n4LBNXWV-Ico",
+        "outputId": "62c0c3a4-c948-4759-c17e-6c3a056ebbc2"
+      },
+      "source": [
+        "data.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 25 entries, 0 to 24\n",
+            "Data columns (total 2 columns):\n",
+            " #   Column  Non-Null Count  Dtype  \n",
+            "---  ------  --------------  -----  \n",
+            " 0   Hours   25 non-null     float64\n",
+            " 1   Scores  25 non-null     int64  \n",
+            "dtypes: float64(1), int64(1)\n",
+            "memory usage: 528.0 bytes\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mKhpPgVb-aUP",
+        "outputId": "8c4774ce-6e11-4dc4-81f4-ba3e0ba6113d"
+      },
+      "source": [
+        "# Checking for the missing values\n",
+        "data.isnull().sum()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Hours     0\n",
+              "Scores    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5M_z4yaiRsgo"
+      },
+      "source": [
+        "### Observation :<br>\n",
+        " - ```There is no missing or null value & hence we don't need to do data preprocessing```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_PvGRvYgIIlJ"
+      },
+      "source": [
+        "## **Data Visualization**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_Fk0VuejFSb0",
+        "scrolled": true,
+        "outputId": "a3db8ac8-e5d6-43d1-ee95-0f3ade9bb94a"
+      },
+      "source": [
+        "x = data.iloc[:,:-1].values  #spliting data in X & Y\n",
+        "y = data.iloc[:,-1].values\n",
+        "print(x[:5])\n",
+        "print(y[:5])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[2.5]\n",
+            " [5.1]\n",
+            " [3.2]\n",
+            " [8.5]\n",
+            " [3.5]]\n",
+            "[21 47 27 75 30]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DKxTzP5XFTCt",
+        "outputId": "d8ba6fcd-922b-4cc3-b994-83b967ba8ace"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x,y,color='red',marker='+')\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A8oGJ35rFTCu"
+      },
+      "source": [
+        "### Observation :\n",
+        " - ```From the graph we can safely assume a positive linear relation between the number of hours studied and percentage of score```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9piEmDnW_eR8"
+      },
+      "source": [
+        "## **Spliting data in x_train , x_test and y_train , y_test** <a class=\"anchor\" id=\"split\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zIkAttPmFsmX"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split  #spliting data in x_train , x_test & y_train,y_test \n",
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25, random_state = 0)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tn2DtByR_bfT"
+      },
+      "source": [
+        "## **Apply Linear Regression on train data**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tCjInXzh_ZDJ",
+        "outputId": "95ec705b-3085-4cb4-9630-e3e2595d297f"
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "lin_reg = LinearRegression()\n",
+        "lin_reg.fit(x_train,y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "8f_pV9WXITyb",
+        "outputId": "0912eed6-e670-4524-f374-4a3eb29659d4"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x_train,y_train,color='purple',marker='+',label='scatter plot')\n",
+        "plt.plot(x_train,lin_reg.predict(x_train),color='green',label='reg_line')\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mkugz8qaIfQ6"
+      },
+      "source": [
+        "## **Apply Linear Regression on test data** <a class=\"anchor\" id=\"LR\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "cEXsiHHJLCLt",
+        "outputId": "6a4d007b-fc57-4925-8fd7-3d22d64403dc"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x_test,y_test,color='blue',marker='+',label='scatter plot')\n",
+        "plt.plot(x_train,lin_reg.predict(x_train),color='purple',label='reg_line')\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8rT1VTy1Im3j"
+      },
+      "source": [
+        "### **Coefficents and y-intercept**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AOEPm-5YIsY_",
+        "outputId": "aca6159f-3c6b-42b2-80bf-8feedca605d2"
+      },
+      "source": [
+        "print('coefficents : ',lin_reg.coef_)\n",
+        "print('y-intercept : ',lin_reg.intercept_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "coefficents :  [9.94167834]\n",
+            "y-intercept :  1.932204253151646\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5ZrUOCd_A1xq",
+        "outputId": "02f18fe8-464c-4f05-a5c1-59782de0e38e"
+      },
+      "source": [
+        "y_pred = lin_reg.predict(x_test)\n",
+        "y_pred"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([16.84472176, 33.74557494, 75.50062397, 26.7864001 , 60.58810646,\n",
+              "       39.71058194, 20.8213931 ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l3tIWYAMIkLV"
+      },
+      "source": [
+        "## Comparing Actual value with Predicted value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 268
+        },
+        "id": "L5M0h9w7BA5b",
+        "outputId": "4930165b-9c2c-428f-f1f1-0d10144cddb5"
+      },
+      "source": [
+        "result = pd.DataFrame({'Actual values':y_test,'Predicted values':y_pred})\n",
+        "result"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual values</th>\n",
+              "      <th>Predicted values</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>20</td>\n",
+              "      <td>16.844722</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27</td>\n",
+              "      <td>33.745575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>69</td>\n",
+              "      <td>75.500624</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>30</td>\n",
+              "      <td>26.786400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>62</td>\n",
+              "      <td>60.588106</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>35</td>\n",
+              "      <td>39.710582</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>24</td>\n",
+              "      <td>20.821393</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Actual values  Predicted values\n",
+              "0             20         16.844722\n",
+              "1             27         33.745575\n",
+              "2             69         75.500624\n",
+              "3             30         26.786400\n",
+              "4             62         60.588106\n",
+              "5             35         39.710582\n",
+              "6             24         20.821393"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qeFWgg9uFTCw",
+        "outputId": "557fc3fe-8d57-435c-cda7-0f5c72a0abf8"
+      },
+      "source": [
+        "# Plotting the Bar graph to depict the difference between the actual and predicted value\n",
+        "\n",
+        "result.plot(kind='bar',figsize=(9,9))\n",
+        "plt.grid(which='major', linewidth='0.5', color='red')\n",
+        "plt.grid(which='minor', linewidth='0.5', color='blue')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 648x648 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bYHZ6ReGB6BA",
+        "outputId": "01b28e37-43a8-4530-9704-24ed723ed90a"
+      },
+      "source": [
+        "diff = np.array(np.abs(y_test-y_pred))\n",
+        "diff"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3.15527824, 6.74557494, 6.50062397, 3.2135999 , 1.41189354,\n",
+              "       4.71058194, 3.1786069 ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YMwEErlDIuph"
+      },
+      "source": [
+        "## Displot distribution of Actual value with Predicted value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "63TpFUeEBUB6",
+        "outputId": "d157d424-e3a8-490d-d6de-2f582ac083ea"
+      },
+      "source": [
+        "sns.set_style('whitegrid')\n",
+        "sns.kdeplot(diff,shade=True)\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jfHjYF6GJLMF"
+      },
+      "source": [
+        "# **Task** <a class=\"anchor\" id=\"PT\"></a>\n",
+        "## - What will be predicted score if a student studies for 9.25 hrs/ day?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HU2vf46WHdAS",
+        "outputId": "0928788d-c3fb-4419-b891-867009e46731"
+      },
+      "source": [
+        "import math\n",
+        "# y = mx + c\n",
+        "res = lin_reg.intercept_+9.25*lin_reg.coef_\n",
+        "hr= 9.25\n",
+        "print(\"If student study for {} hrs/day student will get {}% score in exam\".format(hr,math.floor(res[0])))\n",
+        "print('-'*80)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "If student study for 9.25 hrs/day student will get 93% score in exam\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "884nJWdlFTCx"
+      },
+      "source": [
+        "# Model Evaluation <a class=\"anchor\" id=\"Eval\"></a> "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KBnODZ3ZI8kZ"
+      },
+      "source": [
+        "## MAE :\n",
+        " - MAE measures the differences between prediction and actual observation.<br>\n",
+        " Formula is :<br>\n",
+        " ![1_OVlFLnMwHDx08PHzqlBDag.gif](data:image/gif;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DE_RNvYNLgxN",
+        "outputId": "daa3011b-3812-4511-f5e3-2bdae47d6534"
+      },
+      "source": [
+        "from sklearn import metrics  \n",
+        "print('Mean Absolute Error:', \n",
+        "      metrics.mean_absolute_error(y_test, y_pred)) "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 4.130879918502482\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2oY4inAgJAf4"
+      },
+      "source": [
+        "## MSE :\n",
+        "- MSE simply refers to the mean of the squared difference between the predicted value and the observed value.<br>\n",
+        "Formula : <br>![1_-e1QGatrODWpJkEwqP4Jyg.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dpfYAObBLgp0",
+        "outputId": "c4c4c089-3d6f-439a-cfeb-c3bf102c844f"
+      },
+      "source": [
+        "from sklearn import metrics  \n",
+        "print('Mean Squared Error:', \n",
+        "      metrics.mean_squared_error(y_test, y_pred)) "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 20.33292367497996\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MyCRFiC4Tk8V"
+      },
+      "source": [
+        "## **R-Square** :\n",
+        " - R-squared is  measure of how close the data are to the fitted regression line.<br>\n",
+        " Formula : <br>![images.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_AgoB2wOTkS9",
+        "outputId": "6bdd2cd3-e511-4fc0-c7f9-5d9bda76fb69"
+      },
+      "source": [
+        "from sklearn.metrics import r2_score\n",
+        "r2_score(y_test,y_pred)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9367661043365056"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aYVR0mCXJ7zO"
+      },
+      "source": [
+        "# **Conclusion :**<br><a class=\"anchor\" id=\"conclu\"></a>\n",
+        "- We have successfully created a Simple linear Regression model to predict score of the student given number of hours one studies.\n",
+        "- By the MAE and MSE , we are not getting much difference in actual or predicted value , means error is less.\n",
+        "- The Score of R-Square **0.93** quite close to **1**."
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 2835ec676b2590ab98a134c713e1334c9cfe3a94 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:19:35 +0530
Subject: [PATCH 02/32] Update README.md

---
 README.md | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index c2de2a8..45e2339 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,9 @@
 
 <img height="30" src="https://img.shields.io/badge/The Sparks Foundation-black.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue" />
 
-[![](https://img.shields.io/badge/Author-DeMaria-green.svg)](https://github.com/demaria11)<br>
-[![Hello programmer Welcome to this repo](https://img.shields.io/badge/Hello!-Welcom_here-brightgreen.svg?style=flat&logo=github)](https://demaria11)![](https://img.shields.io/badge/Programming_Language-Python-orange.svg)![](https://img.shields.io/badge/Status-Complete-green.svg)[![](https://img.shields.io/github/License/demaria11/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/demaria11/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/code-size/demaria11/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/demaria11/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/top/demaria11/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/demaria11/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/Follow/demaria11.svg?style=plastic)](https://github.com/demaria11)[![GitHub issues](https://img.shields.io/github/issues/demaria11/THE-SPARKS-FOUNDATION.svg)](https://github.com/demaria11/THE-SPARKS-FOUNDATION/issues) [![GitHub forks](https://img.shields.io/github/forks/demaria11/THE-SPARKS-FOUNDATION.svg)](https://github.com/demaria11/THE-SPARKS-FOUNDATION/network) [![GitHub stars](https://img.shields.io/github/stars/demaria11/THE-SPARKS-FOUNDATION.svg)](https://github.com/demaria11/THE-SPARKS-FOUNDATION/stargazers)
-[![](https://img.shields.io/badge/Followme-Github-green.svg)](https://github.com/demaria11)
+[![](https://img.shields.io/badge/Author-Vedanti-green.svg)](https://github.com/vedanti-github)<br>
+[![Hello programmer Welcome to this repo](https://img.shields.io/badge/Hello!-Welcom_here-brightgreen.svg?style=flat&logo=github)](https://vedanti-github)![](https://img.shields.io/badge/Programming_Language-Python-orange.svg)![](https://img.shields.io/badge/Status-Complete-green.svg)[![](https://img.shields.io/github/License/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/code-size/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/top/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/Follow/vedanti-github.svg?style=plastic)](https://github.com/vedanti-github)[![GitHub issues](https://img.shields.io/github/issues/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/issues) [![GitHub forks](https://img.shields.io/github/forks/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/network) [![GitHub stars](https://img.shields.io/github/stars/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/stargazers)
+[![](https://img.shields.io/badge/Followme-Github-green.svg)](https://github.com/vedanti-github)
 <br><br><br>
 <img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" />
 <br>
@@ -14,9 +14,9 @@
 - You can use R, Python, SAS Enterprise Miner or any other tool.<br>
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
 - Here is the dataset :
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
 
 >Demo:
 <a href="https://youtu.be/yPUkaPVM2EY">Prediction using Supervised ML</a>
@@ -30,9 +30,9 @@
 represent it visually.<br>
 - Use R or Python or perform this task<br>
 - Here is the dataset :
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>
 
 > Demo:
 <a href="https://youtu.be/HV0tD0OQF5w">Prediction using Unsupervised ML</a>
@@ -49,9 +49,9 @@ make more profit.<br>
 - You can choose any of the tool of your choice<br>
 (Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
 - Here is the dataset :
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Retail(Dataset).csv">Dataset.csv</a><br><br><br>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Retail(Dataset).csv">Dataset.csv</a><br><br><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Exploratory%20Data%20Analysis%20-%20Retail.ipynb"> Exploratory Data Analysis-Retail</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Exploratory%20Data%20Analysis%20-%20Retail.ipynb"> Exploratory Data Analysis-Retail</a>
 
 > Demo:
 <a href="https://youtu.be/tPzOUmbOKnQ">Exploratory Data Analysis-Retail</a>
@@ -66,9 +66,9 @@ make more profit.<br>
 predict the right class accordingly. <br>
 - Use R or Python or perform this task<br>
 - Here is the dataset :
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>
 
 > Demo:
 <a href="https://www.youtube.com/watch?v=UFl8yvJF4qk">Prediction using Decision Tree Algorithm</a>
@@ -84,9 +84,9 @@ predict the right class accordingly. <br>
 - You can choose any of the tool of your choice<br>
 (Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
 - Here is the dataset :
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>
 
 > Demo:
 <a href="https://www.youtube.com/watch?v=n_asl4kNRcg">To explore Business Analytics</a>
@@ -104,7 +104,7 @@ predict the right class accordingly. <br>
 - Here is the dataset :
 <a href="https://bit.ly/2TK5Xn5">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Terrorism/Exploratory%20Data%20Analysis%20-%20Terrorism.ipynb">Exploratory Data Analysis - Terrorism</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Terrorism/Exploratory%20Data%20Analysis%20-%20Terrorism.ipynb">Exploratory Data Analysis - Terrorism</a>
 
 > Demo:
 <a href="https://youtu.be/a67W7Tl_g-8 ">Exploratory Data Analysis - Terrorism</a>
@@ -123,7 +123,7 @@ predict the right class accordingly. <br>
 - Here is the dataset :
 <a href="https://bit.ly/34SRn3b">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Sports/Exploratory%20Data%20Analysis%20-%20Sports.ipynb">Exploratory Data Analysis - Sports</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Sports/Exploratory%20Data%20Analysis%20-%20Sports.ipynb">Exploratory Data Analysis - Sports</a>
 
 > Demo:
 <a href="https://youtu.be/aXOHe1lsPBo">Exploratory Data Analysis - Sports</a>
@@ -146,7 +146,7 @@ while not changing the objective of the task.<br>
   - Download textual (news) data from https://bit.ly/36fFPI6
 </a><br>
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Stock%20Market%20Prediction%20using%20Numerical%20and%20Textual%20Analysis/Stock%20Market%20Prediction%20.ipynb">Stock Market Prediction using Numerical and Textual Analysis</a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Stock%20Market%20Prediction%20using%20Numerical%20and%20Textual%20Analysis/Stock%20Market%20Prediction%20.ipynb">Stock Market Prediction using Numerical and Textual Analysis</a>
 
 > Demo:
 <a href="https://youtu.be/4aQkjzaDQIg">Stock Market Prediction using Numerical and Textual Analysis</a>
@@ -165,7 +165,7 @@ basic as well as advanced charts <br>
 
   - Dataset: Daily updated .csv file on https://bit.ly/30d2gdi <br>  
 > Solution:
-<a href="https://github.com/demaria11/THE-SPARKS-FOUNDATION/blob/master/Timeline%20Analysis%20%20Covid-19/COVID19.ipynb">Timeline Analysis : Covid-19 </a>
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Timeline%20Analysis%20%20Covid-19/COVID19.ipynb">Timeline Analysis : Covid-19 </a>
 
 > Demo:
 <a href="">Timeline Analysis : Covid-19</a>
@@ -182,9 +182,9 @@ basic as well as advanced charts <br>
 <hr />
 
 [youtube]: https://www.youtube.com/channel/UCIHj6mNCMnSnmWLHOxzIESw?view_as=subscriber
-[gmail]: mailto:devesh6571@gmail.com
-[linkedin]: https://www.linkedin.com/in//
-[github]: https://github.com/demaria11/
+[gmail]: mailto:vedantiekre@gmail.com
+[linkedin]:www.linkedin.com/in/vedanti-ekre
+[github]: https://github.com/vedanti-github/
 
 
 

From 826834d19630a518267738c8c936358e78038bed Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:24:13 +0530
Subject: [PATCH 03/32] Rename student_scores - student_scores.csv to
 student_scores.csv

---
 .../{student_scores - student_scores.csv => student_scores.csv} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename Prediction using Supervised ML/{student_scores - student_scores.csv => student_scores.csv} (96%)

diff --git a/Prediction using Supervised ML/student_scores - student_scores.csv b/Prediction using Supervised ML/student_scores.csv
similarity index 96%
rename from Prediction using Supervised ML/student_scores - student_scores.csv
rename to Prediction using Supervised ML/student_scores.csv
index 41be33c..5d44c04 100644
--- a/Prediction using Supervised ML/student_scores - student_scores.csv	
+++ b/Prediction using Supervised ML/student_scores.csv	
@@ -23,4 +23,4 @@ Hours,Scores
 4.8,54
 3.8,35
 6.9,76
-7.8,86
\ No newline at end of file
+7.8,86

From b76960697184bd2d483f7ffb6e9750e520aef7e7 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:31:38 +0530
Subject: [PATCH 04/32] Create README.md

---
 Prediction using Supervised ML/README.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 Prediction using Supervised ML/README.md

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
new file mode 100644
index 0000000..8f239fe
--- /dev/null
+++ b/Prediction using Supervised ML/README.md	
@@ -0,0 +1,14 @@
+<img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" />
+<br>
+> Problem statement :
+- Predict the percentage of an student based on the no. of study hours. <br>
+- This is a simple linear regression task as it involves just 2 variables.<br>
+- You can use R, Python, SAS Enterprise Miner or any other tool.<br>
+- What will be predicted score if a student studies for 9.25 hrs/ day? <br>
+- Here is the dataset :
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
+> Solution:
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
+
+>Demo:
+<a href="https://youtu.be/yPUkaPVM2EY">Prediction using Supervised ML</a>

From 6b0cbeeeeddf03c469035987ae8f1e6c0f8816ff Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:32:32 +0530
Subject: [PATCH 05/32] Update README.md

---
 Prediction using Supervised ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index 8f239fe..0bd655b 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -6,7 +6,7 @@
 - You can use R, Python, SAS Enterprise Miner or any other tool.<br>
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores.csv>Dataset.csv</a><br><br>
 > Solution:
 <a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
 

From 80f0544c766e3b2127708746c632a6df22de2186 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:42:06 +0530
Subject: [PATCH 06/32] Update README.md

---
 Prediction using Supervised ML/README.md | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index 0bd655b..ef59923 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -1,14 +1,24 @@
 <img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" />
 <br>
+
+# Author : Vedanti Ekre
+
+# Email: vedantiekre@gmail.com
+
+## Task 1 : Prediction using Supervised Machine Learning
+___
+## GRIP @ The Sparks Foundation
+____
+# Role : Data Science and Business Analytics [Batch May-2021]
+
 > Problem statement :
 - Predict the percentage of an student based on the no. of study hours. <br>
 - This is a simple linear regression task as it involves just 2 variables.<br>
 - You can use R, Python, SAS Enterprise Miner or any other tool.<br>
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
-- Here is the dataset :
-<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores.csv>Dataset.csv</a><br><br>
-> Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
 
->Demo:
-<a href="https://youtu.be/yPUkaPVM2EY">Prediction using Supervised ML</a>
+> Here is the dataset :
+<a href="https://github.com/PrajwalGKale/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores%20.csv">Dataset.csv</a><br>
+
+> Solution:
+<a href="https://github.com/PrajwalGKale/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Task_01.ipynb"> Prediction using Supervised ML</a>

From 35ccb36eb51b1015e64512aefd852246b1adfcf7 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:43:19 +0530
Subject: [PATCH 07/32] Update README.md

---
 Prediction using Supervised ML/README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index ef59923..14219e1 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -1,5 +1,4 @@
-<img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" />
-<br>
+<img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" /><br>
 
 # Author : Vedanti Ekre
 

From 734fc021396f35638e390f3240d313e2566de36a Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:44:37 +0530
Subject: [PATCH 08/32] Update README.md

---
 Prediction using Supervised ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index 14219e1..c609b26 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -20,4 +20,4 @@ ____
 <a href="https://github.com/PrajwalGKale/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores%20.csv">Dataset.csv</a><br>
 
 > Solution:
-<a href="https://github.com/PrajwalGKale/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Task_01.ipynb"> Prediction using Supervised ML</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Task_01.ipynb"> Prediction using Supervised ML</a>

From 889a3f63b2aeaea7910b8017384a81c40195a88a Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:45:30 +0530
Subject: [PATCH 09/32] Update README.md

---
 Prediction using Supervised ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index c609b26..d95da32 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -17,7 +17,7 @@ ____
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
 
 > Here is the dataset :
-<a href="https://github.com/PrajwalGKale/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores%20.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores.csv">Dataset.csv</a><br>
 
 > Solution:
 <a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Task_01.ipynb"> Prediction using Supervised ML</a>

From 3962d81ffdc0466a02a7e61824f050541d2158bc Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:46:42 +0530
Subject: [PATCH 10/32] Delete student_scores.csv

---
 .../student_scores.csv                        | 26 -------------------
 1 file changed, 26 deletions(-)
 delete mode 100644 Prediction using Supervised ML/student_scores.csv

diff --git a/Prediction using Supervised ML/student_scores.csv b/Prediction using Supervised ML/student_scores.csv
deleted file mode 100644
index 5d44c04..0000000
--- a/Prediction using Supervised ML/student_scores.csv	
+++ /dev/null
@@ -1,26 +0,0 @@
-Hours,Scores
-2.5,21
-5.1,47
-3.2,27
-8.5,75
-3.5,30
-1.5,20
-9.2,88
-5.5,60
-8.3,81
-2.7,25
-7.7,85
-5.9,62
-4.5,41
-3.3,42
-1.1,17
-8.9,95
-2.5,30
-1.9,24
-6.1,67
-7.4,69
-2.7,30
-4.8,54
-3.8,35
-6.9,76
-7.8,86

From 2e96c92adbce245d31fd8f553189db91948fc74a Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:48:34 +0530
Subject: [PATCH 11/32] Add files via upload

---
 .../Student_scores.csv.csv                    | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 Prediction using Supervised ML/Student_scores.csv.csv

diff --git a/Prediction using Supervised ML/Student_scores.csv.csv b/Prediction using Supervised ML/Student_scores.csv.csv
new file mode 100644
index 0000000..2de5f0e
--- /dev/null
+++ b/Prediction using Supervised ML/Student_scores.csv.csv	
@@ -0,0 +1,26 @@
+Hours,Scores
+2.5,21
+5.1,47
+3.2,27
+8.5,75
+3.5,30
+1.5,20
+9.2,88
+5.5,60
+8.3,81
+2.7,25
+7.7,85
+5.9,62
+4.5,41
+3.3,42
+1.1,17
+8.9,95
+2.5,30
+1.9,24
+6.1,67
+7.4,69
+2.7,30
+4.8,54
+3.8,35
+6.9,76
+7.8,86

From 9c4c551bf9f3d5fbdef69da7343dba377fb70b74 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:48:51 +0530
Subject: [PATCH 12/32] Delete Student_scores.csv.csv

---
 .../Student_scores.csv.csv                    | 26 -------------------
 1 file changed, 26 deletions(-)
 delete mode 100644 Prediction using Supervised ML/Student_scores.csv.csv

diff --git a/Prediction using Supervised ML/Student_scores.csv.csv b/Prediction using Supervised ML/Student_scores.csv.csv
deleted file mode 100644
index 2de5f0e..0000000
--- a/Prediction using Supervised ML/Student_scores.csv.csv	
+++ /dev/null
@@ -1,26 +0,0 @@
-Hours,Scores
-2.5,21
-5.1,47
-3.2,27
-8.5,75
-3.5,30
-1.5,20
-9.2,88
-5.5,60
-8.3,81
-2.7,25
-7.7,85
-5.9,62
-4.5,41
-3.3,42
-1.1,17
-8.9,95
-2.5,30
-1.9,24
-6.1,67
-7.4,69
-2.7,30
-4.8,54
-3.8,35
-6.9,76
-7.8,86

From 44953464ad2a1ecd9f95a0d2d548d22e6f5292f6 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:49:46 +0530
Subject: [PATCH 13/32] Add files via upload

---
 .../Student_scores.csv                        | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 Prediction using Supervised ML/Student_scores.csv

diff --git a/Prediction using Supervised ML/Student_scores.csv b/Prediction using Supervised ML/Student_scores.csv
new file mode 100644
index 0000000..2de5f0e
--- /dev/null
+++ b/Prediction using Supervised ML/Student_scores.csv	
@@ -0,0 +1,26 @@
+Hours,Scores
+2.5,21
+5.1,47
+3.2,27
+8.5,75
+3.5,30
+1.5,20
+9.2,88
+5.5,60
+8.3,81
+2.7,25
+7.7,85
+5.9,62
+4.5,41
+3.3,42
+1.1,17
+8.9,95
+2.5,30
+1.9,24
+6.1,67
+7.4,69
+2.7,30
+4.8,54
+3.8,35
+6.9,76
+7.8,86

From 498182322b9cc2782da185843948c933b2e52b45 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 12:50:26 +0530
Subject: [PATCH 14/32] Update README.md

---
 Prediction using Supervised ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index d95da32..da4faa1 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -17,7 +17,7 @@ ____
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
 
 > Here is the dataset :
-<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/student_scores.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Student_scores.csv">Dataset.csv</a><br>
 
 > Solution:
 <a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Supervised%20ML/Task_01.ipynb"> Prediction using Supervised ML</a>

From a95f56f4d6b5d75bae9b9dcf3093f4484a234930 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 13:32:38 +0530
Subject: [PATCH 15/32] Update README.md

---
 Prediction using Supervised ML/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Prediction using Supervised ML/README.md b/Prediction using Supervised ML/README.md
index da4faa1..3960370 100644
--- a/Prediction using Supervised ML/README.md	
+++ b/Prediction using Supervised ML/README.md	
@@ -1,4 +1,5 @@
 <img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" /><br>
+<h1 align="left">Hi <img src="https://github.com/demaria11/Spark_Projects/blob/main/Hi.gif" width="35px">, I am Vedanti & you're Welcome here !!! </h1>
 
 # Author : Vedanti Ekre
 

From 485d697e8f74f16c49d13b0ddc0ae0b8eaaaccf8 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 13:39:35 +0530
Subject: [PATCH 16/32] Delete Task_01.ipynb

---
 Prediction using Supervised ML/Task_01.ipynb | 1046 ------------------
 1 file changed, 1046 deletions(-)
 delete mode 100644 Prediction using Supervised ML/Task_01.ipynb

diff --git a/Prediction using Supervised ML/Task_01.ipynb b/Prediction using Supervised ML/Task_01.ipynb
deleted file mode 100644
index 2de89af..0000000
--- a/Prediction using Supervised ML/Task_01.ipynb	
+++ /dev/null
@@ -1,1046 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Task_01.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.2"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Z1dOavLaFTCn"
-      },
-      "source": [
-        "# Author : Vedanti Ekre\n",
-        "\n",
-        "#Email: vedantiekre@gmail.com\n",
-        "\n",
-        "## Task 1 : Prediction using Supervised Machine Learning\n",
-        "___\n",
-        "## GRIP @ The Sparks Foundation\n",
-        "____\n",
-        "# Role : Data Science and Business Analytics [Batch May-2021]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6EY1YO2Z9tga"
-      },
-      "source": [
-        "## TABLE OF CONTENTS:\n",
-        "\n",
-        "1. [Introduction](#intro)\n",
-        "2. [Importing the dependencies](#libs)\n",
-        "3. [Loading the Data](#DL)\n",
-        "4. [Understanding data](#UD)\n",
-        "5. [Spliting data in Test and Train](#split)\n",
-        "6. [Use Smiple Linear Regression Model to do prediction](#LR)\n",
-        "7. [Task](#PT)\n",
-        "8. [Evaluate the model using MAE and MSE metrics](#Eval)\n",
-        "9. [Conslusion](#conclu)\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DX7H6U-tPXx6"
-      },
-      "source": [
-        "## **Introduction**<a class=\"anchor\" id=\"intro\"></a>\n",
-        "● We have given Student dataset,which have only two features Hours and scores.<br>\n",
-        "● Predict the percentage of an student based on the no. of study hours.<br>\n",
-        "● This is a simple linear regression task as it involves just 2 variables.<br>\n",
-        "● You can use R, Python, SAS Enterprise Miner or any other tool<br>\n",
-        "● Data can be found at http://bit.ly/w-data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fPIIqDQCFTCq"
-      },
-      "source": [
-        "## Importing dependencies<a class=\"anchor\" id=\"libs\"></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "2lveKmLX7U_1"
-      },
-      "source": [
-        "#importing packages\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "%matplotlib inline"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PWkEOHEj-Ar1"
-      },
-      "source": [
-        "## **Loading the Data**<a class=\"anchor\" id=\"DL\"></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "GX_rdxGU75EK"
-      },
-      "source": [
-        "#importing datasets\n",
-        "url = \"http://bit.ly/w-data\"\n",
-        "data = pd.read_csv(url)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1bJoiktf-Gmb"
-      },
-      "source": [
-        "## **Understanding data**<a class=\"anchor\" id=\"UD\"></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 205
-        },
-        "id": "OdreYCnA-H9G",
-        "outputId": "294cc237-5efc-469c-df7a-a8229376d730"
-      },
-      "source": [
-        "display(data.head(3),data.tail(3))"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Hours</th>\n",
-              "      <th>Scores</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>2.5</td>\n",
-              "      <td>21</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>5.1</td>\n",
-              "      <td>47</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>3.2</td>\n",
-              "      <td>27</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Hours  Scores\n",
-              "0    2.5      21\n",
-              "1    5.1      47\n",
-              "2    3.2      27"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Hours</th>\n",
-              "      <th>Scores</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>22</th>\n",
-              "      <td>3.8</td>\n",
-              "      <td>35</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>23</th>\n",
-              "      <td>6.9</td>\n",
-              "      <td>76</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>24</th>\n",
-              "      <td>7.8</td>\n",
-              "      <td>86</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "    Hours  Scores\n",
-              "22    3.8      35\n",
-              "23    6.9      76\n",
-              "24    7.8      86"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Xgy95nk1-KBF",
-        "outputId": "533b0c5c-574e-483f-b6ad-f8b4d26bc1c6"
-      },
-      "source": [
-        "print(type(data))\n",
-        "print('-'*45)\n",
-        "print('The data set has {} rows and {} columns'.format(data.shape[0],data.shape[1]))\n",
-        "print('-'*45)\n",
-        "print('Data types :') \n",
-        "print(data.dtypes.value_counts())\n",
-        "#print('Total : ',data.dtypes.value_counts().sum())\n",
-        "print('-'*45)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "---------------------------------------------\n",
-            "The data set has 25 rows and 2 columns\n",
-            "---------------------------------------------\n",
-            "Data types :\n",
-            "int64      1\n",
-            "float64    1\n",
-            "dtype: int64\n",
-            "---------------------------------------------\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "n4LBNXWV-Ico",
-        "outputId": "62c0c3a4-c948-4759-c17e-6c3a056ebbc2"
-      },
-      "source": [
-        "data.info()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 25 entries, 0 to 24\n",
-            "Data columns (total 2 columns):\n",
-            " #   Column  Non-Null Count  Dtype  \n",
-            "---  ------  --------------  -----  \n",
-            " 0   Hours   25 non-null     float64\n",
-            " 1   Scores  25 non-null     int64  \n",
-            "dtypes: float64(1), int64(1)\n",
-            "memory usage: 528.0 bytes\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mKhpPgVb-aUP",
-        "outputId": "8c4774ce-6e11-4dc4-81f4-ba3e0ba6113d"
-      },
-      "source": [
-        "# Checking for the missing values\n",
-        "data.isnull().sum()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Hours     0\n",
-              "Scores    0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5M_z4yaiRsgo"
-      },
-      "source": [
-        "### Observation :<br>\n",
-        " - ```There is no missing or null value & hence we don't need to do data preprocessing```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_PvGRvYgIIlJ"
-      },
-      "source": [
-        "## **Data Visualization**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_Fk0VuejFSb0",
-        "scrolled": true,
-        "outputId": "a3db8ac8-e5d6-43d1-ee95-0f3ade9bb94a"
-      },
-      "source": [
-        "x = data.iloc[:,:-1].values  #spliting data in X & Y\n",
-        "y = data.iloc[:,-1].values\n",
-        "print(x[:5])\n",
-        "print(y[:5])"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "[[2.5]\n",
-            " [5.1]\n",
-            " [3.2]\n",
-            " [8.5]\n",
-            " [3.5]]\n",
-            "[21 47 27 75 30]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DKxTzP5XFTCt",
-        "outputId": "d8ba6fcd-922b-4cc3-b994-83b967ba8ace"
-      },
-      "source": [
-        "plt.xlabel('hours')\n",
-        "plt.ylabel('scores')\n",
-        "plt.scatter(x,y,color='red',marker='+')\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAXsUlEQVR4nO3df5Afd33f8ecL24DtM5WM7avAJHITiQKe+sdX40CY2DqEGZI4MdDYgQRXYdxeJvWAnbSJRTodyHQycWZSCu2kSRycViTgQ/hHrNCMY0Wc5GZanOj8IzYIywN2FGNjEecEOpuCZV79Y/fgdHfSrU63393vd1+PmZ397n73+93XyfJb7/t89/tZ2SYiIrrjJU0HiIiI/krhj4jomBT+iIiOSeGPiOiYFP6IiI45uekAVZx11lleu3ZtpWOfe+45Tj/99HoDLUNyVdfGTNDOXG3MBO3M1cZMUG+uqampf7B99oInbLd+6fV6rmpycrLysf2UXNW1MZPdzlxtzGS3M1cbM9n15gL2eJGamqGeiIiOSeGPiOiYFP6IiI5J4Y+I6JgU/oiIjknhj4jol40bi6VhKfwRER0zEF/giogYaLNd/u7dR27v2tVAmHT8ERGdk44/IqJus519w53+rHT8EREdk44/IqJfGu70Z6Xjj4jomBT+iIiOSeGPiOiYFP6IiI5J4Y+I6JgU/oiIjqm18Eu6XtIjkr4g6YZy35mSdkh6rFyvrjNDREQcqbbCL+l84N8AlwAXAFdIWgdsAXbaXgfsLLcjIqJP6uz4Xwd83vbztg8Du4F3AlcCW8tjtgLvqDFDRETMo+JG7DW8sfQ64C7gTcC3KLr7PcA1tlfNOW7a9oLhHknjwDjA6Ohob2JiotJ5Z2ZmGBkZOfEfYIUlV3VtzATtzNXGTNDOXG3MBPXmGhsbm7K9YcETtmtbgGuB+4F7gd8H/gtwcN4x00u9T6/Xc1WTk5OVj+2n5KqujZnsduZqYya7nbnamMmuNxewx4vU1Fo/3LV9i+2LbV8K/CPwGPCMpDUA5fpAnRkiIgZWTXfsqvuqnnPK9Q8A7wJuBbYDm8tDNlMMB0VERJ/UPTvn7ZJeCbwAXGd7WtJNwDZJ1wL7gatqzhARMVhqvmNXrYXf9o8tsu9ZYFOd542IiKPLfPwREW1T8x27MmVDRAyHmj4IHUbp+CMi2qqmO3al8EfEYKv5g9BhlMIfEYMpwzrLlsIfEYOt5g9Ch1EKf0QMlqMN7URlKfwRMRzS6VeWwh8RgyVDOycs1/FHRHRMOv6IGEzp9JctHX9ERMek8EdEdEwKf0REx6TwR0TMN+QTvqXwR0R0TK1X9Uj6ZeBfAwYeBt4HnAZ8GlgLPAFcbXu6zhwREZV0ZMK32jp+Sa8GPgBssH0+cBLwbmALsNP2OmBnuR0REX1S93X8JwOnSnqBotN/CvggsLF8fiuwC7ix5hwREUvryLeCZbu+N5euB34T+BZwj+2fl3TQ9qo5x0zbXr3Ia8eBcYDR0dHexMREpXPOzMwwMjKyIvlXUnJV18ZM0M5cbcwE7cx1XJn27SvW69fXF6hU55/V2NjYlO0NC56wXcsCrAY+B5wNnAL8KfBe4OC846aXeq9er+eqJicnKx/bT8lVXRsz2e3M1cZMdjtztTGTXW8uYI8Xqal1XtXzVuBx21+3/QJwB/CjwDOS1gCU6wM1ZoiIiHnqLPz7gTdKOk2SgE3AXmA7sLk8ZjNwV40ZIiJinto+3LV9n6TbgPuBw8ADwM3ACLBN0rUU/zhcVVeGiIhYqNaremx/CPjQvN3fpuj+IyKiAfnmbkREx6TwR8SJGfJ5bYZRCn9ERMfkDlwRsTwdmddmGKXjj4g4HkMwtJWOPyKWpyPz2gyjFP6IiCqGaGgrhT8iTswAFr6uS+GPiKhiiIa28uFuRETHpOOPiDgeA9zpz0rHHxHRMSn8EREdk8IfEdExKfwRER2Twh8R0TG1FX5Jr5X04Jzlm5JukHSmpB2SHivXq+vKEBERC9VW+G0/avtC2xcCPeB54E5gC7DT9jpgZ7kdERF90q+hnk3Al23/HXAlsLXcvxV4R58yREQE/Sv87wZuLR+P2n4aoFyf06cMEdF2QzDl8SCQ7XpPIL0UeAp4g+1nJB20vWrO89O2F4zzSxoHxgFGR0d7ExMTlc43MzPDyMjIyoRfQclVXRszQTtztTETnECuffuK9fr1KxuIIfyzqmBsbGzK9oYFT9iudaEY2rlnzvajwJry8Rrg0aXeo9fruarJycnKx/ZTclXXxkx2O3O1MZO9jFyXXVYsUCyz201m6pM6cwF7vEhN7cdQz3v4/jAPwHZgc/l4M3BXHzJERESp1knaJJ0GXA784pzdNwHbJF0L7AeuqjNDRAyAIZryeBDUWvhtPw+8ct6+Zymu8omIlZSiGRVlWuaIaI/8o9UXKfwRg26I7gUb/ZG5eiIiOiYdf8SgywejcZzS8UdEdEw6/ohhkU4/KkrHHxHRMSn8EREdk8IfEdExKfwRER2Twh8R0TEp/BERHZPCHzFIcoeqWAEp/BERHZMvcEUMgqNNxPbhDzcQJgZdOv6IiI5Jxx8xCI42EVumaYhlqNTxS/ohSS8rH2+U9AFJqyq8bpWk2yR9SdJeSW+SdKakHZIeK9erT/SHiBgo+YA2GlZ1qOd24EVJPwzcApwHfKrC6z4G3G37nwMXAHuBLcBO2+uAneV2RFSxa1e6/DhhVYd6vmv7sKR3Ah+1/d8kPXCsF0h6BXAp8AsAtr8DfEfSlcDG8rCtwC7gxuOPHjFgcqesaAnZXvog6T7go8B/AH7K9uOSHrF9/jFecyFwM/BFim5/Crge+KrtVXOOm7a9YLhH0jgwDjA6OtqbmJio9APNzMwwMjJS6dh+Sq7q2pgJViDXvn3F+tChYn3GGcV6/frmMtWkjbnamAnqzTU2NjZle8OCJ2wvuQCvB/4r8J5y+zxgyxKv2QAcBn6k3P4Y8J+Ag/OOm17q/L1ez1VNTk5WPrafkqu6NmayVzDXZZcVywoY+j+rFdTGTHa9uYA9XqSmVhrjt/1FiuGY+8vtx23ftMTLngSetH1fuX0bcDHwjKQ1AOX6QJUMERGxMqpe1fNTwIPA3eX2hZK2H+s1tr8G/L2k15a7NlEM+2wHNpf7NgN3LSN3xODKB7TRsKof7n4YuITig1hsPyjpvAqvez/wSUkvBb4CvI/iH5ttkq4F9gNXHWfmiIg4AVUL/2Hb35A0d9+SnwrbfpBirH++TRXPGxERK6xq4X9E0s8BJ0laB3wA+D/1xYqIiLpU/QLX+4E3AN+m+OLWN4Ab6goVERH1WbLjl3QSsN32Wymu44+IiAG2ZMdv+0XgeUn/pA95IiKiZlXH+P8f8LCkHcBzszttf6CWVBERUZuqhf9/lUtERAy4SoXf9tbyWvzZSUUetf1CfbEi+iwTpkWHVCr8kjZSzKT5BCDgNZI22763vmgREVGHqkM9/xl4m+1HASStB24FenUFi+iLTJUcHVT1Ov5TZos+gO19wCn1RIqIiDpV7fj3SLoF+ONy++cp5tePGGxHu5dtxBCrWvh/CbiOYqoGAfcC/72uUBERUZ+qhf9k4GO2PwLf+zbvy2pLFdFv6fSjQ6qO8e8ETp2zfSrwlysfJyIi6la18L/c9szsRvn4tHoiRUREnaoW/uckXTy7IWkD8K16IkVERJ2qjvFfD3xG0lMUN2B5FfCzS71I0hPAIeBFipu5bJB0JvBpYC3FF8Kutj193MkjImJZqnb85wEXUVzdswN4lAp34CqN2b7Q9uyduLYAO22vo/jsYMtx5I2IiBNUtfD/R9vfBFYBlwM3A7+3zHNeSTH9A+X6Hct8n4iIWAbZSzfukh6wfZGk3wIetv2p2X1LvO5xYJrit4M/sH2zpIO2V805Ztr26kVeOw6MA4yOjvYmJiYq/UAzMzOMjIxUOrafkqu6NmaCduZqYyZoZ642ZoJ6c42NjU3NGW35PttLLsBngT8AvkzR9b8MeKjC615Vrs8BHgIuBQ7OO2Z6qffp9XquanJysvKx/ZRc1bUxk93OXG3MZLczVxsz2fXmAvZ4kZpadajnauAvgLfbPgicCfzqUi+y/VS5PgDcCVwCPCNpDUC5PlAxQ0RErIBKhd/287bvsP1Yuf207XuO9RpJp0s6Y/Yx8DbgEWA7sLk8bDNw13LDR0TE8at6OedyjAJ3Spo9z6ds3y3pb4Btkq4F9gNX1ZghIiLmqa3w2/4KcMEi+58FNtV13ojaZSbPGHBVx/gjImJIpPBHVLVvX9Ht795dLBs3fr/7jxggKfwRER1T54e7EcNl/fpiXD9j/DHg0vFHRHRMOv6I45VOPwZcOv6IiI5J4Y+I6JgU/oiIjknhj4jomBT+iIiOSeGPiOiYFP6oJtMTRAyNFP6IiI7JF7ji2Ga7/N27j9wehC8xDVLWiD5Kxx8R0TG1d/ySTgL2AF+1fYWkM4FPA2uBJ4CrbU/XnSOWabZbHqTueZB/S4nog350/NcDe+dsbwF22l4H7Cy3IyKiT2rt+CWdC/wk8JvAr5S7rwQ2lo+3AruAG+vMEStgkLrlQfwtJaKP6u74Pwr8GvDdOftGbT8NUK7PqTlDRETMIdv1vLF0BfATtv+tpI3Avy/H+A/aXjXnuGnbqxd5/TgwDjA6OtqbmJiodN6ZmRlGRkZW5GdYSclVXRszQTtztTETtDNXGzNBvbnGxsambG9Y8ITtWhbgt4AnKT7A/RrwPPAnwKPAmvKYNcCjS71Xr9dzVZOTk5WP7afkqq6Nmex25mpjJrududqYya43F7DHi9TU2oZ6bH/Q9rm21wLvBj5n+73AdmBzedhm4K66MkRExEJNXMd/E3C5pMeAy8vtiIjok758c9f2Loqrd7D9LLCpH+eNiIiF8s3diIiOSeGPiOiYFP6IiI5J4Y+I6JgU/ui/3NQlolEp/BERHZMbsUT/ZLrkiFZIxx8R0THp+KN/Ml1yRCuk44+I6Jh0/NF/6fQjGpWOPyKiY1L4IyI6JoU/IqJjUvgjIjomhT8iomNS+CMiOqa2wi/p5ZL+WtJDkr4g6TfK/WdK2iHpsXK9uq4MQ+lYE5w1NflZJl2LGCh1dvzfBt5i+wLgQuDtkt4IbAF22l4H7Cy3IyKiT2r7ApdtAzPl5inlYuBKYGO5fyvFvXhvrCvH0DjWBGdNTX6WSdciBpKK+lzTm0snAVPADwO/a/tGSQdtr5pzzLTtBcM9ksaBcYDR0dHexMREpXPOzMwwMjKyIvlX0gnn2revWB86VKzPOKNYr19/7OfqzHUC560tU43amKuNmaCdudqYCerNNTY2NmV7w4InbNe+AKuASeB84OC856aXen2v13NVk5OTlY/tpxXLddllxXK8zx3FiuRaxnmPZej/G66gNmay25mrjZnsenMBe7xITe3LVT22D1IM6bwdeEbSGoByfaAfGSIiolDbGL+ks4EXbB+UdCrwVuC3ge3AZuCmcn1XXRmG0rHGz5saW8+YfsRAqXN2zjXA1nKc/yXANtuflfR/gW2SrgX2A1fVmCEiIuap86qevwUuWmT/s8Cmus4bKyRX6EQMrXxzNyKiY3IjljhSrs2PGHrp+CMiOiYdfxwpN0SPGHrp+IdJJkuLiArS8cfi0ulHDK0U/jr0e5gkH8hGxHHIUE9ERMek419JTXXe+UA2Io5DOv6IiI5Jx7+Smu680+lHRAXp+CMiOiYdfx3SeUdEi6Xjj4jomBT+QZNv50bECUrhj4jomDpvvfga4BPAPwW+C9xs+2OSzgQ+DawFngCutj1dV45la9s18fl2bkSskDo7/sPAv7P9OuCNwHWSXg9sAXbaXgfsLLcjIqJP6rz14tPA0+XjQ5L2Aq8GrgQ2lodtBXYBN9aV47i1tbNu+jsCETE0ZLv+k0hrgXuB84H9tlfNeW7a9upFXjMOjAOMjo72JiYmKp1rZmaGkZGR5Yfdt69YHzpUrM84o1ivX7/891yJXLNm851gnlkrlmsFtTETtDNXGzNBO3O1MRPUm2tsbGzK9oYFT9iudQFGgCngXeX2wXnPTy/1Hr1ez1VNTk5WPvaYLrusWFbIiuVaYW3M1cZMdjtztTGT3c5cbcxk15sL2ONFamqtV/VIOgW4Hfik7TvK3c9IWlM+vwY4UGeGiIg4Up1X9Qi4Bdhr+yNzntoObAZuKtd31ZXhhGQMPSKGVJ1TNrwZuAZ4WNKD5b5fpyj42yRdC+wHrqoxQ0REzFPnVT1/BegoT2+q67xHyBUwEREL5Ju7EREdM5yzc7b1WvyIiBZIxx8R0THD2fHnW64REUeVjj8iomOGs+OflU4/ImKBdPwRER2Twh8R0TEp/BERHZPCHxHRMSn8EREdk8IfEdExfbkD14mS9HXg7yoefhbwDzXGWa7kqq6NmaCdudqYCdqZq42ZoN5cP2j77Pk7B6LwHw9Je7zYrcYallzVtTETtDNXGzNBO3O1MRM0kytDPRERHZPCHxHRMcNY+G9uOsBRJFd1bcwE7czVxkzQzlxtzAQN5Bq6Mf6IiDi2Yez4IyLiGFL4IyI6ZmgKv6Q/knRA0iNNZ5lL0mskTUraK+kLkq5vQaaXS/prSQ+VmX6j6UyzJJ0k6QFJn206yyxJT0h6WNKDkvY0nWeWpFWSbpP0pfLv15sazvPa8s9odvmmpBuazDRL0i+Xf9cfkXSrpJe3INP1ZZ4v9PvPaWjG+CVdCswAn7B9ftN5ZklaA6yxfb+kM4Ap4B22v9hgJgGn256RdArwV8D1tj/fVKZZkn4F2AC8wvYVTeeBovADG2y36ss/krYC/9v2xyW9FDjN9sGmc0HxDzjwVeBHbFf98mVdWV5N8Xf89ba/JWkb8Oe2/2eDmc4HJoBLgO8AdwO/ZPuxfpx/aDp+2/cC/9h0jvlsP237/vLxIWAv8OqGM9n2TLl5Srk03gFIOhf4SeDjTWdpO0mvAC4FbgGw/Z22FP3SJuDLTRf9OU4GTpV0MnAa8FTDeV4HfN7287YPA7uBd/br5ENT+AeBpLXARcB9zSb53pDKg8ABYIftxjMBHwV+Dfhu00HmMXCPpClJ402HKf0z4OvA/yiHxj4u6fSmQ83xbuDWpkMA2P4q8DvAfuBp4Bu272k2FY8Al0p6paTTgJ8AXtOvk6fw94mkEeB24Abb32w6j+0XbV8InAtcUv7q2RhJVwAHbE81meMo3mz7YuDHgevKYcWmnQxcDPye7YuA54AtzUYqlMNOPw18puksAJJWA1cC5wGvAk6X9N4mM9neC/w2sINimOch4HC/zp/C3wflOPrtwCdt39F0nrnK4YFdwNsbjvJm4KfL8fQJ4C2S/qTZSAXbT5XrA8CdFOOyTXsSeHLOb2q3UfxD0AY/Dtxv+5mmg5TeCjxu++u2XwDuAH604UzYvsX2xbYvpRim7sv4PqTw1678IPUWYK/tjzSdB0DS2ZJWlY9Ppfgf40tNZrL9Qdvn2l5LMUzwOduNdmUAkk4vP5SnHEp5G8Wv6Y2y/TXg7yW9tty1CWjsgoF53kNLhnlK+4E3Sjqt/P9xE8VnbY2SdE65/gHgXfTxz+zkfp2obpJuBTYCZ0l6EviQ7VuaTQUUnew1wMPlmDrAr9v+8wYzrQG2lldevATYZrs1l0+2zChwZ1EvOBn4lO27m430Pe8HPlkOrXwFeF/DeSjHqy8HfrHpLLNs3yfpNuB+iuGUB2jH9A23S3ol8AJwne3pfp14aC7njIiIajLUExHRMSn8EREdk8IfEdExKfwRER2Twh8R0TEp/BElSWvbNrtrRB1S+CNqVE4KFtEqKfwRRzpJ0h+Wc6TfI+lUSRdK+rykv5V0Zzn3C5J2SdpQPj6rnG4CSb8g6TOS/oxicrc1ku4t56h/RNKPNffjRaTwR8y3Dvhd228ADgL/EvgEcKPtfwE8DHyowvu8Cdhs+y3AzwF/UU6KdwHw4DFfGVGz/BoacaTHbc8W5ingh4BVtneX+7ZSbdbJHbZn7w/xN8AflZP1/emc949oRDr+iCN9e87jF4FVxzj2MN//f2j+rfyem31Q3iToUoo7Uv2xpH+1Ajkjli2FP+LYvgFMzxmXv4bibkkATwC98vHPHO0NJP0gxb0G/pBipta2TJ8cHZWhnoilbQZ+v5x5cu4smL8DbJN0DfC5Y7x+I/Crkl6guC90Ov5oVGbnjIjomAz1RER0TAp/RETHpPBHRHRMCn9ERMek8EdEdEwKf0REx6TwR0R0zP8HQrLhk1sOyZAAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A8oGJ35rFTCu"
-      },
-      "source": [
-        "### Observation :\n",
-        " - ```From the graph we can safely assume a positive linear relation between the number of hours studied and percentage of score```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9piEmDnW_eR8"
-      },
-      "source": [
-        "## **Spliting data in x_train , x_test and y_train , y_test** <a class=\"anchor\" id=\"split\"></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "zIkAttPmFsmX"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split  #spliting data in x_train , x_test & y_train,y_test \n",
-        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25, random_state = 0)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tn2DtByR_bfT"
-      },
-      "source": [
-        "## **Apply Linear Regression on train data**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "tCjInXzh_ZDJ",
-        "outputId": "95ec705b-3085-4cb4-9630-e3e2595d297f"
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "lin_reg = LinearRegression()\n",
-        "lin_reg.fit(x_train,y_train)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression()"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 33
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 279
-        },
-        "id": "8f_pV9WXITyb",
-        "outputId": "0912eed6-e670-4524-f374-4a3eb29659d4"
-      },
-      "source": [
-        "plt.xlabel('hours')\n",
-        "plt.ylabel('scores')\n",
-        "plt.scatter(x_train,y_train,color='purple',marker='+',label='scatter plot')\n",
-        "plt.plot(x_train,lin_reg.predict(x_train),color='green',label='reg_line')\n",
-        "plt.legend()\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Mkugz8qaIfQ6"
-      },
-      "source": [
-        "## **Apply Linear Regression on test data** <a class=\"anchor\" id=\"LR\"></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 279
-        },
-        "id": "cEXsiHHJLCLt",
-        "outputId": "6a4d007b-fc57-4925-8fd7-3d22d64403dc"
-      },
-      "source": [
-        "plt.xlabel('hours')\n",
-        "plt.ylabel('scores')\n",
-        "plt.scatter(x_test,y_test,color='blue',marker='+',label='scatter plot')\n",
-        "plt.plot(x_train,lin_reg.predict(x_train),color='purple',label='reg_line')\n",
-        "plt.legend()\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8rT1VTy1Im3j"
-      },
-      "source": [
-        "### **Coefficents and y-intercept**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AOEPm-5YIsY_",
-        "outputId": "aca6159f-3c6b-42b2-80bf-8feedca605d2"
-      },
-      "source": [
-        "print('coefficents : ',lin_reg.coef_)\n",
-        "print('y-intercept : ',lin_reg.intercept_)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "coefficents :  [9.94167834]\n",
-            "y-intercept :  1.932204253151646\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5ZrUOCd_A1xq",
-        "outputId": "02f18fe8-464c-4f05-a5c1-59782de0e38e"
-      },
-      "source": [
-        "y_pred = lin_reg.predict(x_test)\n",
-        "y_pred"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([16.84472176, 33.74557494, 75.50062397, 26.7864001 , 60.58810646,\n",
-              "       39.71058194, 20.8213931 ])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 37
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "l3tIWYAMIkLV"
-      },
-      "source": [
-        "## Comparing Actual value with Predicted value"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 268
-        },
-        "id": "L5M0h9w7BA5b",
-        "outputId": "4930165b-9c2c-428f-f1f1-0d10144cddb5"
-      },
-      "source": [
-        "result = pd.DataFrame({'Actual values':y_test,'Predicted values':y_pred})\n",
-        "result"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Actual values</th>\n",
-              "      <th>Predicted values</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>20</td>\n",
-              "      <td>16.844722</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>27</td>\n",
-              "      <td>33.745575</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>69</td>\n",
-              "      <td>75.500624</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>30</td>\n",
-              "      <td>26.786400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>62</td>\n",
-              "      <td>60.588106</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>35</td>\n",
-              "      <td>39.710582</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>24</td>\n",
-              "      <td>20.821393</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Actual values  Predicted values\n",
-              "0             20         16.844722\n",
-              "1             27         33.745575\n",
-              "2             69         75.500624\n",
-              "3             30         26.786400\n",
-              "4             62         60.588106\n",
-              "5             35         39.710582\n",
-              "6             24         20.821393"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 38
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qeFWgg9uFTCw",
-        "outputId": "557fc3fe-8d57-435c-cda7-0f5c72a0abf8"
-      },
-      "source": [
-        "# Plotting the Bar graph to depict the difference between the actual and predicted value\n",
-        "\n",
-        "result.plot(kind='bar',figsize=(9,9))\n",
-        "plt.grid(which='major', linewidth='0.5', color='red')\n",
-        "plt.grid(which='minor', linewidth='0.5', color='blue')\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 648x648 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bYHZ6ReGB6BA",
-        "outputId": "01b28e37-43a8-4530-9704-24ed723ed90a"
-      },
-      "source": [
-        "diff = np.array(np.abs(y_test-y_pred))\n",
-        "diff"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([3.15527824, 6.74557494, 6.50062397, 3.2135999 , 1.41189354,\n",
-              "       4.71058194, 3.1786069 ])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 45
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YMwEErlDIuph"
-      },
-      "source": [
-        "## Displot distribution of Actual value with Predicted value"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 369
-        },
-        "id": "63TpFUeEBUB6",
-        "outputId": "d157d424-e3a8-490d-d6de-2f582ac083ea"
-      },
-      "source": [
-        "sns.set_style('whitegrid')\n",
-        "sns.kdeplot(diff,shade=True)\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jfHjYF6GJLMF"
-      },
-      "source": [
-        "# **Task** <a class=\"anchor\" id=\"PT\"></a>\n",
-        "## - What will be predicted score if a student studies for 9.25 hrs/ day?"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HU2vf46WHdAS",
-        "outputId": "0928788d-c3fb-4419-b891-867009e46731"
-      },
-      "source": [
-        "import math\n",
-        "# y = mx + c\n",
-        "res = lin_reg.intercept_+9.25*lin_reg.coef_\n",
-        "hr= 9.25\n",
-        "print(\"If student study for {} hrs/day student will get {}% score in exam\".format(hr,math.floor(res[0])))\n",
-        "print('-'*80)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "If student study for 9.25 hrs/day student will get 93% score in exam\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "884nJWdlFTCx"
-      },
-      "source": [
-        "# Model Evaluation <a class=\"anchor\" id=\"Eval\"></a> "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KBnODZ3ZI8kZ"
-      },
-      "source": [
-        "## MAE :\n",
-        " - MAE measures the differences between prediction and actual observation.<br>\n",
-        " Formula is :<br>\n",
-        " ![1_OVlFLnMwHDx08PHzqlBDag.gif](data:image/gif;base64,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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DE_RNvYNLgxN",
-        "outputId": "daa3011b-3812-4511-f5e3-2bdae47d6534"
-      },
-      "source": [
-        "from sklearn import metrics  \n",
-        "print('Mean Absolute Error:', \n",
-        "      metrics.mean_absolute_error(y_test, y_pred)) "
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mean Absolute Error: 4.130879918502482\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2oY4inAgJAf4"
-      },
-      "source": [
-        "## MSE :\n",
-        "- MSE simply refers to the mean of the squared difference between the predicted value and the observed value.<br>\n",
-        "Formula : <br>![1_-e1QGatrODWpJkEwqP4Jyg.png](data:image/png;base64,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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dpfYAObBLgp0",
-        "outputId": "c4c4c089-3d6f-439a-cfeb-c3bf102c844f"
-      },
-      "source": [
-        "from sklearn import metrics  \n",
-        "print('Mean Squared Error:', \n",
-        "      metrics.mean_squared_error(y_test, y_pred)) "
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mean Absolute Error: 20.33292367497996\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MyCRFiC4Tk8V"
-      },
-      "source": [
-        "## **R-Square** :\n",
-        " - R-squared is  measure of how close the data are to the fitted regression line.<br>\n",
-        " Formula : <br>![images.png](data:image/png;base64,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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_AgoB2wOTkS9",
-        "outputId": "6bdd2cd3-e511-4fc0-c7f9-5d9bda76fb69"
-      },
-      "source": [
-        "from sklearn.metrics import r2_score\n",
-        "r2_score(y_test,y_pred)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.9367661043365056"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 73
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aYVR0mCXJ7zO"
-      },
-      "source": [
-        "# **Conclusion :**<br><a class=\"anchor\" id=\"conclu\"></a>\n",
-        "- We have successfully created a Simple linear Regression model to predict score of the student given number of hours one studies.\n",
-        "- By the MAE and MSE , we are not getting much difference in actual or predicted value , means error is less.\n",
-        "- The Score of R-Square **0.93** quite close to **1**."
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 78672cb36b8a8414aeecc4855681a7bb2f4e4c54 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 13:40:07 +0530
Subject: [PATCH 17/32] Add files via upload

---
 Prediction using Supervised ML/Task_01.ipynb | 1046 ++++++++++++++++++
 1 file changed, 1046 insertions(+)
 create mode 100644 Prediction using Supervised ML/Task_01.ipynb

diff --git a/Prediction using Supervised ML/Task_01.ipynb b/Prediction using Supervised ML/Task_01.ipynb
new file mode 100644
index 0000000..4ad4c90
--- /dev/null
+++ b/Prediction using Supervised ML/Task_01.ipynb	
@@ -0,0 +1,1046 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Task_01.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z1dOavLaFTCn"
+      },
+      "source": [
+        "# Author : Vedanti Ekre\n",
+        "\n",
+        "# Email: vedantiekre@gmail.com\n",
+        "\n",
+        "## Task 1 : Prediction using Supervised Machine Learning\n",
+        "___\n",
+        "## GRIP @ The Sparks Foundation\n",
+        "____\n",
+        "# Role : Data Science and Business Analytics [Batch May-2021]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6EY1YO2Z9tga"
+      },
+      "source": [
+        "## TABLE OF CONTENTS:\n",
+        "\n",
+        "1. [Introduction](#intro)\n",
+        "2. [Importing the dependencies](#libs)\n",
+        "3. [Loading the Data](#DL)\n",
+        "4. [Understanding data](#UD)\n",
+        "5. [Spliting data in Test and Train](#split)\n",
+        "6. [Use Simple Linear Regression Model to do prediction](#LR)\n",
+        "7. [Task](#PT)\n",
+        "8. [Evaluate the model using MAE and MSE metrics](#Eval)\n",
+        "9. [Conslusion](#conclu)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DX7H6U-tPXx6"
+      },
+      "source": [
+        "## **Introduction**<a class=\"anchor\" id=\"intro\"></a>\n",
+        "● We have given Student dataset,which have only two features Hours and scores.<br>\n",
+        "● Predict the percentage of an student based on the no. of study hours.<br>\n",
+        "● This is a simple linear regression task as it involves just 2 variables.<br>\n",
+        "● You can use R, Python, SAS Enterprise Miner or any other tool<br>\n",
+        "● Data can be found at http://bit.ly/w-data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fPIIqDQCFTCq"
+      },
+      "source": [
+        "## Importing dependencies<a class=\"anchor\" id=\"libs\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2lveKmLX7U_1"
+      },
+      "source": [
+        "#importing packages\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PWkEOHEj-Ar1"
+      },
+      "source": [
+        "## **Loading the Data**<a class=\"anchor\" id=\"DL\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GX_rdxGU75EK"
+      },
+      "source": [
+        "#importing datasets\n",
+        "url = \"http://bit.ly/w-data\"\n",
+        "data = pd.read_csv(url)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1bJoiktf-Gmb"
+      },
+      "source": [
+        "## **Understanding data**<a class=\"anchor\" id=\"UD\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 205
+        },
+        "id": "OdreYCnA-H9G",
+        "outputId": "294cc237-5efc-469c-df7a-a8229376d730"
+      },
+      "source": [
+        "display(data.head(3),data.tail(3))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Hours</th>\n",
+              "      <th>Scores</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.5</td>\n",
+              "      <td>21</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>47</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.2</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Hours  Scores\n",
+              "0    2.5      21\n",
+              "1    5.1      47\n",
+              "2    3.2      27"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Hours</th>\n",
+              "      <th>Scores</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>3.8</td>\n",
+              "      <td>35</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>6.9</td>\n",
+              "      <td>76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>86</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Hours  Scores\n",
+              "22    3.8      35\n",
+              "23    6.9      76\n",
+              "24    7.8      86"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xgy95nk1-KBF",
+        "outputId": "533b0c5c-574e-483f-b6ad-f8b4d26bc1c6"
+      },
+      "source": [
+        "print(type(data))\n",
+        "print('-'*45)\n",
+        "print('The data set has {} rows and {} columns'.format(data.shape[0],data.shape[1]))\n",
+        "print('-'*45)\n",
+        "print('Data types :') \n",
+        "print(data.dtypes.value_counts())\n",
+        "#print('Total : ',data.dtypes.value_counts().sum())\n",
+        "print('-'*45)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "---------------------------------------------\n",
+            "The data set has 25 rows and 2 columns\n",
+            "---------------------------------------------\n",
+            "Data types :\n",
+            "int64      1\n",
+            "float64    1\n",
+            "dtype: int64\n",
+            "---------------------------------------------\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n4LBNXWV-Ico",
+        "outputId": "62c0c3a4-c948-4759-c17e-6c3a056ebbc2"
+      },
+      "source": [
+        "data.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 25 entries, 0 to 24\n",
+            "Data columns (total 2 columns):\n",
+            " #   Column  Non-Null Count  Dtype  \n",
+            "---  ------  --------------  -----  \n",
+            " 0   Hours   25 non-null     float64\n",
+            " 1   Scores  25 non-null     int64  \n",
+            "dtypes: float64(1), int64(1)\n",
+            "memory usage: 528.0 bytes\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mKhpPgVb-aUP",
+        "outputId": "8c4774ce-6e11-4dc4-81f4-ba3e0ba6113d"
+      },
+      "source": [
+        "# Checking for the missing values\n",
+        "data.isnull().sum()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Hours     0\n",
+              "Scores    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5M_z4yaiRsgo"
+      },
+      "source": [
+        "### Observation :<br>\n",
+        " - ```There is no missing or null value & hence we don't need to do data preprocessing```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_PvGRvYgIIlJ"
+      },
+      "source": [
+        "## **Data Visualization**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_Fk0VuejFSb0",
+        "scrolled": true,
+        "outputId": "a3db8ac8-e5d6-43d1-ee95-0f3ade9bb94a"
+      },
+      "source": [
+        "x = data.iloc[:,:-1].values  #spliting data in X & Y\n",
+        "y = data.iloc[:,-1].values\n",
+        "print(x[:5])\n",
+        "print(y[:5])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[2.5]\n",
+            " [5.1]\n",
+            " [3.2]\n",
+            " [8.5]\n",
+            " [3.5]]\n",
+            "[21 47 27 75 30]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DKxTzP5XFTCt",
+        "outputId": "d8ba6fcd-922b-4cc3-b994-83b967ba8ace"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x,y,color='red',marker='+')\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A8oGJ35rFTCu"
+      },
+      "source": [
+        "### Observation :\n",
+        " - ```From the graph we can safely assume a positive linear relation between the number of hours studied and percentage of score```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9piEmDnW_eR8"
+      },
+      "source": [
+        "## **Spliting data in x_train , x_test and y_train , y_test** <a class=\"anchor\" id=\"split\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zIkAttPmFsmX"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split  #spliting data in x_train , x_test & y_train,y_test \n",
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25, random_state = 0)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tn2DtByR_bfT"
+      },
+      "source": [
+        "## **Apply Linear Regression on train data**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tCjInXzh_ZDJ",
+        "outputId": "95ec705b-3085-4cb4-9630-e3e2595d297f"
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "lin_reg = LinearRegression()\n",
+        "lin_reg.fit(x_train,y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "8f_pV9WXITyb",
+        "outputId": "0912eed6-e670-4524-f374-4a3eb29659d4"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x_train,y_train,color='purple',marker='+',label='scatter plot')\n",
+        "plt.plot(x_train,lin_reg.predict(x_train),color='green',label='reg_line')\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mkugz8qaIfQ6"
+      },
+      "source": [
+        "## **Apply Linear Regression on test data** <a class=\"anchor\" id=\"LR\"></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "cEXsiHHJLCLt",
+        "outputId": "6a4d007b-fc57-4925-8fd7-3d22d64403dc"
+      },
+      "source": [
+        "plt.xlabel('hours')\n",
+        "plt.ylabel('scores')\n",
+        "plt.scatter(x_test,y_test,color='blue',marker='+',label='scatter plot')\n",
+        "plt.plot(x_train,lin_reg.predict(x_train),color='purple',label='reg_line')\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8rT1VTy1Im3j"
+      },
+      "source": [
+        "### **Coefficents and y-intercept**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AOEPm-5YIsY_",
+        "outputId": "aca6159f-3c6b-42b2-80bf-8feedca605d2"
+      },
+      "source": [
+        "print('coefficents : ',lin_reg.coef_)\n",
+        "print('y-intercept : ',lin_reg.intercept_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "coefficents :  [9.94167834]\n",
+            "y-intercept :  1.932204253151646\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5ZrUOCd_A1xq",
+        "outputId": "02f18fe8-464c-4f05-a5c1-59782de0e38e"
+      },
+      "source": [
+        "y_pred = lin_reg.predict(x_test)\n",
+        "y_pred"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([16.84472176, 33.74557494, 75.50062397, 26.7864001 , 60.58810646,\n",
+              "       39.71058194, 20.8213931 ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l3tIWYAMIkLV"
+      },
+      "source": [
+        "## Comparing Actual value with Predicted value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 268
+        },
+        "id": "L5M0h9w7BA5b",
+        "outputId": "4930165b-9c2c-428f-f1f1-0d10144cddb5"
+      },
+      "source": [
+        "result = pd.DataFrame({'Actual values':y_test,'Predicted values':y_pred})\n",
+        "result"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Actual values</th>\n",
+              "      <th>Predicted values</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>20</td>\n",
+              "      <td>16.844722</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27</td>\n",
+              "      <td>33.745575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>69</td>\n",
+              "      <td>75.500624</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>30</td>\n",
+              "      <td>26.786400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>62</td>\n",
+              "      <td>60.588106</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>35</td>\n",
+              "      <td>39.710582</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>24</td>\n",
+              "      <td>20.821393</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Actual values  Predicted values\n",
+              "0             20         16.844722\n",
+              "1             27         33.745575\n",
+              "2             69         75.500624\n",
+              "3             30         26.786400\n",
+              "4             62         60.588106\n",
+              "5             35         39.710582\n",
+              "6             24         20.821393"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qeFWgg9uFTCw",
+        "outputId": "557fc3fe-8d57-435c-cda7-0f5c72a0abf8"
+      },
+      "source": [
+        "# Plotting the Bar graph to depict the difference between the actual and predicted value\n",
+        "\n",
+        "result.plot(kind='bar',figsize=(9,9))\n",
+        "plt.grid(which='major', linewidth='0.5', color='red')\n",
+        "plt.grid(which='minor', linewidth='0.5', color='blue')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 648x648 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bYHZ6ReGB6BA",
+        "outputId": "01b28e37-43a8-4530-9704-24ed723ed90a"
+      },
+      "source": [
+        "diff = np.array(np.abs(y_test-y_pred))\n",
+        "diff"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3.15527824, 6.74557494, 6.50062397, 3.2135999 , 1.41189354,\n",
+              "       4.71058194, 3.1786069 ])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YMwEErlDIuph"
+      },
+      "source": [
+        "## Displot distribution of Actual value with Predicted value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "63TpFUeEBUB6",
+        "outputId": "d157d424-e3a8-490d-d6de-2f582ac083ea"
+      },
+      "source": [
+        "sns.set_style('whitegrid')\n",
+        "sns.kdeplot(diff,shade=True)\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jfHjYF6GJLMF"
+      },
+      "source": [
+        "# **Task** <a class=\"anchor\" id=\"PT\"></a>\n",
+        "## - What will be predicted score if a student studies for 9.25 hrs/ day?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HU2vf46WHdAS",
+        "outputId": "0928788d-c3fb-4419-b891-867009e46731"
+      },
+      "source": [
+        "import math\n",
+        "# y = mx + c\n",
+        "res = lin_reg.intercept_+9.25*lin_reg.coef_\n",
+        "hr= 9.25\n",
+        "print(\"If student study for {} hrs/day student will get {}% score in exam\".format(hr,math.floor(res[0])))\n",
+        "print('-'*80)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "If student study for 9.25 hrs/day student will get 93% score in exam\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "884nJWdlFTCx"
+      },
+      "source": [
+        "# Model Evaluation <a class=\"anchor\" id=\"Eval\"></a> "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KBnODZ3ZI8kZ"
+      },
+      "source": [
+        "## MAE :\n",
+        " - MAE measures the differences between prediction and actual observation.<br>\n",
+        " Formula is :<br>\n",
+        " ![1_OVlFLnMwHDx08PHzqlBDag.gif](data:image/gif;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DE_RNvYNLgxN",
+        "outputId": "daa3011b-3812-4511-f5e3-2bdae47d6534"
+      },
+      "source": [
+        "from sklearn import metrics  \n",
+        "print('Mean Absolute Error:', \n",
+        "      metrics.mean_absolute_error(y_test, y_pred)) "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 4.130879918502482\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2oY4inAgJAf4"
+      },
+      "source": [
+        "## MSE :\n",
+        "- MSE simply refers to the mean of the squared difference between the predicted value and the observed value.<br>\n",
+        "Formula : <br>![1_-e1QGatrODWpJkEwqP4Jyg.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dpfYAObBLgp0",
+        "outputId": "c4c4c089-3d6f-439a-cfeb-c3bf102c844f"
+      },
+      "source": [
+        "from sklearn import metrics  \n",
+        "print('Mean Squared Error:', \n",
+        "      metrics.mean_squared_error(y_test, y_pred)) "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 20.33292367497996\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MyCRFiC4Tk8V"
+      },
+      "source": [
+        "## **R-Square** :\n",
+        " - R-squared is  measure of how close the data are to the fitted regression line.<br>\n",
+        " Formula : <br>![images.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_AgoB2wOTkS9",
+        "outputId": "6bdd2cd3-e511-4fc0-c7f9-5d9bda76fb69"
+      },
+      "source": [
+        "from sklearn.metrics import r2_score\n",
+        "r2_score(y_test,y_pred)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9367661043365056"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aYVR0mCXJ7zO"
+      },
+      "source": [
+        "# **Conclusion :**<br><a class=\"anchor\" id=\"conclu\"></a>\n",
+        "- We have successfully created a Simple linear Regression model to predict score of the student given number of hours one studies.\n",
+        "- By the MAE and MSE , we are not getting much difference in actual or predicted value , means error is less.\n",
+        "- The Score of R-Square **0.93** quite close to **1**."
+      ]
+    }
+  ]
+}
\ No newline at end of file

From dc8db51e8c0f2c606b07f46bf4086dc02d07dffa Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 14:23:31 +0530
Subject: [PATCH 18/32] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 45e2339..d641a04 100644
--- a/README.md
+++ b/README.md
@@ -183,7 +183,7 @@ basic as well as advanced charts <br>
 
 [youtube]: https://www.youtube.com/channel/UCIHj6mNCMnSnmWLHOxzIESw?view_as=subscriber
 [gmail]: mailto:vedantiekre@gmail.com
-[linkedin]:www.linkedin.com/in/vedanti-ekre
+[linkedin]:www.linkedin.com/in/vedanti-ekre/
 [github]: https://github.com/vedanti-github/
 
 

From 2efb77fd8eb97f1ba35bdabb989c5de5dde8c152 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 14:27:10 +0530
Subject: [PATCH 19/32] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d641a04..ae33448 100644
--- a/README.md
+++ b/README.md
@@ -183,7 +183,7 @@ basic as well as advanced charts <br>
 
 [youtube]: https://www.youtube.com/channel/UCIHj6mNCMnSnmWLHOxzIESw?view_as=subscriber
 [gmail]: mailto:vedantiekre@gmail.com
-[linkedin]:www.linkedin.com/in/vedanti-ekre/
+[linkedin]:https://www.linkedin.com/in/vedanti-ekre/
 [github]: https://github.com/vedanti-github/
 
 

From cec92c38d4772880431fbe5da3a58ee522f0081a Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:31:04 +0530
Subject: [PATCH 20/32] Create README.md

---
 Prediction using Unsupervised ML/README.md | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 Prediction using Unsupervised ML/README.md

diff --git a/Prediction using Unsupervised ML/README.md b/Prediction using Unsupervised ML/README.md
new file mode 100644
index 0000000..43e4044
--- /dev/null
+++ b/Prediction using Unsupervised ML/README.md	
@@ -0,0 +1,11 @@
+<img height="27" src="https://img.shields.io/badge/Prediction using Unsupervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
+<br>
+
+> Problem Statement:
+- From the given ‘Iris’ dataset, predict the optimum number of clusters and
+represent it visually.<br>
+- Use R or Python or perform this task<br>
+- Here is the dataset :
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
+> Solution:
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>

From a9f8b7bf09230a374c0cad70489a936dd497698f Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:35:13 +0530
Subject: [PATCH 21/32] Update README.md

---
 Prediction using Unsupervised ML/README.md | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/Prediction using Unsupervised ML/README.md b/Prediction using Unsupervised ML/README.md
index 43e4044..e42b504 100644
--- a/Prediction using Unsupervised ML/README.md	
+++ b/Prediction using Unsupervised ML/README.md	
@@ -1,11 +1,22 @@
-<img height="27" src="https://img.shields.io/badge/Prediction using Unsupervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
-<br>
+<img height="27" src="https://img.shields.io/badge/Prediction using Unsupervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/><br>
+<h1 align="left">Hi <img src="https://github.com/demaria11/Spark_Projects/blob/main/Hi.gif" width="35px">, I am Vedanti & you're Welcome here !!! </h1>
 
+# Author : Vedanti Ekre
+
+# Email: vedantiekre@gmail.com
+
+## Task 1 : Prediction using Unsupervised Machine Learning
+___
+## GRIP @ The Sparks Foundation
+____
+# Role : Data Science and Business Analytics [Batch May-2021]
 > Problem Statement:
 - From the given ‘Iris’ dataset, predict the optimum number of clusters and
 represent it visually.<br>
 - Use R or Python or perform this task<br>
-- Here is the dataset :
-<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
+
+> Here is the dataset :
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br>
+
 > Solution:
 <a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>

From 85d68f10f35f5ee1fec871f8c2b3b2b085fff88a Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:38:09 +0530
Subject: [PATCH 22/32] Create README.md

---
 .../README.md                                 | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 Prediction using Decision Tree Algorithm/README.md

diff --git a/Prediction using Decision Tree Algorithm/README.md b/Prediction using Decision Tree Algorithm/README.md
new file mode 100644
index 0000000..ee4484d
--- /dev/null
+++ b/Prediction using Decision Tree Algorithm/README.md	
@@ -0,0 +1,23 @@
+<img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" /><br>
+<h1 align="left">Hi <img src="https://github.com/demaria11/Spark_Projects/blob/main/Hi.gif" width="35px">, I am Vedanti & you're Welcome here !!! </h1>
+
+# Author : Vedanti Ekre
+
+# Email: vedantiekre@gmail.com
+
+## Task 1 : Prediction using Decision Tree Algorithm
+___
+## GRIP @ The Sparks Foundation
+____
+# Role : Data Science and Business Analytics [Batch May-2021]
+
+> Problem Statement:
+- Create the Decision Tree classifier and visualize it graphically.<br>
+- The purpose is if we feed any new data to this classifier, it would be able to
+predict the right class accordingly. <br>
+- Use R or Python or perform this task<br>
+> Here is the dataset :
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br>
+
+> Solution:
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>

From 817f212011a1691931c5b8e55a323fd67a2fe07d Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:45:17 +0530
Subject: [PATCH 23/32] Create README.md

---
 Prediction using Decision Tree Algorithm/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Decision Tree Algorithm/README.md b/Prediction using Decision Tree Algorithm/README.md
index ee4484d..5b6d313 100644
--- a/Prediction using Decision Tree Algorithm/README.md	
+++ b/Prediction using Decision Tree Algorithm/README.md	
@@ -17,7 +17,7 @@ ____
 predict the right class accordingly. <br>
 - Use R or Python or perform this task<br>
 > Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br>
 
 > Solution:
 <a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>

From 5c8c0b0540135ea620ca9dbb8fa9652c50f69bc2 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:46:08 +0530
Subject: [PATCH 24/32] Update README.md

---
 Prediction using Decision Tree Algorithm/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Decision Tree Algorithm/README.md b/Prediction using Decision Tree Algorithm/README.md
index 5b6d313..80197b1 100644
--- a/Prediction using Decision Tree Algorithm/README.md	
+++ b/Prediction using Decision Tree Algorithm/README.md	
@@ -5,7 +5,7 @@
 
 # Email: vedantiekre@gmail.com
 
-## Task 1 : Prediction using Decision Tree Algorithm
+## Task 3 : Prediction using Decision Tree Algorithm
 ___
 ## GRIP @ The Sparks Foundation
 ____

From 360570f89f388984585907c5553f4f13751d2234 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:46:46 +0530
Subject: [PATCH 25/32] Update README.md

---
 Prediction using Unsupervised ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Prediction using Unsupervised ML/README.md b/Prediction using Unsupervised ML/README.md
index e42b504..f34dce9 100644
--- a/Prediction using Unsupervised ML/README.md	
+++ b/Prediction using Unsupervised ML/README.md	
@@ -5,7 +5,7 @@
 
 # Email: vedantiekre@gmail.com
 
-## Task 1 : Prediction using Unsupervised Machine Learning
+## Task 2 : Prediction using Unsupervised Machine Learning
 ___
 ## GRIP @ The Sparks Foundation
 ____

From 5c14e98af9f6ea03d4eee0eb4747957f5419380d Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:48:14 +0530
Subject: [PATCH 26/32] Create README.md

---
 Exploratory Data Analysis - Retail/README.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Exploratory Data Analysis - Retail/README.md

diff --git a/Exploratory Data Analysis - Retail/README.md b/Exploratory Data Analysis - Retail/README.md
new file mode 100644
index 0000000..c16129c
--- /dev/null
+++ b/Exploratory Data Analysis - Retail/README.md	
@@ -0,0 +1,13 @@
+<img height="27" src="https://img.shields.io/badge/To explore Business Analytics -Level  Intermediate-orange.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
+<br>
+
+> Problem Statement:
+- Perform ‘explore Business Analytics’ on dataset ‘superstore.csv’ <br>
+
+- What all business problems you can derive by exploring the data?<br>
+- You can choose any of the tool of your choice<br>
+(Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
+> Here is the dataset :
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
+> Solution:
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>

From d45341aade10d83bb51f9ab8bc8d42fcee527df8 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:48:58 +0530
Subject: [PATCH 27/32] Update README.md

---
 Exploratory Data Analysis - Retail/README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/Exploratory Data Analysis - Retail/README.md b/Exploratory Data Analysis - Retail/README.md
index c16129c..453df59 100644
--- a/Exploratory Data Analysis - Retail/README.md	
+++ b/Exploratory Data Analysis - Retail/README.md	
@@ -3,11 +3,12 @@
 
 > Problem Statement:
 - Perform ‘explore Business Analytics’ on dataset ‘superstore.csv’ <br>
-
 - What all business problems you can derive by exploring the data?<br>
 - You can choose any of the tool of your choice<br>
 (Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
+
 > Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/main/Exploratory%20Data%20Analysis%20-%20Retail/Retail(Dataset).csv">Dataset.csv</a><br>
+
 > Solution:
 <a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>

From 670e581461a5262e7eafe510d888ee878a81b3ac Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:50:20 +0530
Subject: [PATCH 28/32] Create README.md

---
 Exploratory Data Analysis - Terrorism/README.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 Exploratory Data Analysis - Terrorism/README.md

diff --git a/Exploratory Data Analysis - Terrorism/README.md b/Exploratory Data Analysis - Terrorism/README.md
new file mode 100644
index 0000000..77434ac
--- /dev/null
+++ b/Exploratory Data Analysis - Terrorism/README.md	
@@ -0,0 +1,14 @@
+<img height="27" src="https://img.shields.io/badge/Exploratory Data Analysis (Terrorism) -Level  Intermediate-orange.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/><br>
+
+> Problem Statement:
+- Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’ <br>
+- As a security/defense analyst, try to find out the hot zone of terrorism.<br>
+- What all security issues and insights you can derive by EDA?<br>
+- You can choose any of the tool of your choice
+(Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
+
+> Here is the dataset :
+<a href="https://bit.ly/2TK5Xn5">Dataset.csv</a><br>
+
+> Solution:
+<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Terrorism/Exploratory%20Data%20Analysis%20-%20Terrorism.ipynb">Exploratory Data Analysis - Terrorism</a>

From aeefc83407e568052031922d53512bcc5486531d Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:55:44 +0530
Subject: [PATCH 29/32] Update README.md

---
 Exploratory Data Analysis - Retail/README.md | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/Exploratory Data Analysis - Retail/README.md b/Exploratory Data Analysis - Retail/README.md
index 453df59..e56aa48 100644
--- a/Exploratory Data Analysis - Retail/README.md	
+++ b/Exploratory Data Analysis - Retail/README.md	
@@ -1,5 +1,15 @@
-<img height="27" src="https://img.shields.io/badge/To explore Business Analytics -Level  Intermediate-orange.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
-<br>
+<img height="27" src="https://img.shields.io/badge/To explore Business Analytics -Level  Intermediate-orange.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/><br>
+<h1 align="left">Hi <img src="https://github.com/demaria11/Spark_Projects/blob/main/Hi.gif" width="35px">, I am Vedanti & you're Welcome here !!! </h1>
+
+# Author : Vedanti Ekre
+
+# Email: vedantiekre@gmail.com
+
+## Task 2 : Exploratory Data Analysis
+___
+## GRIP @ The Sparks Foundation
+____
+# Role : Data Science and Business Analytics [Batch May-2021]
 
 > Problem Statement:
 - Perform ‘explore Business Analytics’ on dataset ‘superstore.csv’ <br>

From ce0120724fda66328f36487e241c53831ed382c9 Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 20:56:17 +0530
Subject: [PATCH 30/32] Update README.md

---
 Exploratory Data Analysis - Retail/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Exploratory Data Analysis - Retail/README.md b/Exploratory Data Analysis - Retail/README.md
index e56aa48..e3bb0d6 100644
--- a/Exploratory Data Analysis - Retail/README.md	
+++ b/Exploratory Data Analysis - Retail/README.md	
@@ -5,7 +5,7 @@
 
 # Email: vedantiekre@gmail.com
 
-## Task 2 : Exploratory Data Analysis
+## Task 5 : Exploratory Data Analysis
 ___
 ## GRIP @ The Sparks Foundation
 ____

From 9758043cf672ff85cb57bd51fdd5088a243cba0b Mon Sep 17 00:00:00 2001
From: vedanti-github <65652310+vedanti-github@users.noreply.github.com>
Date: Sun, 20 Jun 2021 21:00:21 +0530
Subject: [PATCH 31/32] Update README.md

---
 Exploratory Data Analysis - Terrorism/README.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/Exploratory Data Analysis - Terrorism/README.md b/Exploratory Data Analysis - Terrorism/README.md
index 77434ac..c201999 100644
--- a/Exploratory Data Analysis - Terrorism/README.md	
+++ b/Exploratory Data Analysis - Terrorism/README.md	
@@ -1,4 +1,16 @@
 <img height="27" src="https://img.shields.io/badge/Exploratory Data Analysis (Terrorism) -Level  Intermediate-orange.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/><br>
+<h1 align="left">Hi <img src="https://github.com/demaria11/Spark_Projects/blob/main/Hi.gif" width="35px">, I am Vedanti & you're Welcome here !!! </h1>
+
+# Author : Vedanti Ekre
+
+# Email: vedantiekre@gmail.com
+
+## Task 4 : Exploratory Data Analysis
+___
+## GRIP @ The Sparks Foundation
+____
+# Role : Data Science and Business Analytics [Batch May-2021]
+
 
 > Problem Statement:
 - Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’ <br>

From d32d6d80618b92a2178622acc2729b26dc29309c Mon Sep 17 00:00:00 2001
From: lol <devesh6571@gmail.com>
Date: Mon, 21 Jun 2021 13:18:35 +0530
Subject: [PATCH 32/32] Update README.md

---
 README.md | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index ae33448..c2cd6fb 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 <img height="30" src="https://img.shields.io/badge/The Sparks Foundation-black.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue" />
 
 [![](https://img.shields.io/badge/Author-Vedanti-green.svg)](https://github.com/vedanti-github)<br>
-[![Hello programmer Welcome to this repo](https://img.shields.io/badge/Hello!-Welcom_here-brightgreen.svg?style=flat&logo=github)](https://vedanti-github)![](https://img.shields.io/badge/Programming_Language-Python-orange.svg)![](https://img.shields.io/badge/Status-Complete-green.svg)[![](https://img.shields.io/github/License/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/code-size/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/languages/top/vedanti-github/THE-SPARKS-FOUNDATION.svg?style=plastic)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION)[![](https://img.shields.io/github/Follow/vedanti-github.svg?style=plastic)](https://github.com/vedanti-github)[![GitHub issues](https://img.shields.io/github/issues/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/issues) [![GitHub forks](https://img.shields.io/github/forks/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/network) [![GitHub stars](https://img.shields.io/github/stars/vedanti-github/THE-SPARKS-FOUNDATION.svg)](https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/stargazers)
+[![Hello programmer Welcome to this repo](https://img.shields.io/badge/Hello!-Welcom_here-brightgreen.svg?style=flat&logo=github)](https://vedanti-github)![](https://img.shields.io/badge/Programming_Language-Python-orange.svg)![](https://img.shields.io/badge/Status-Complete-green.svg)[![](https://img.shields.io/github/License/vedanti-github/Spark_Projects.svg?style=plastic)](https://github.com/vedanti-github/Spark_Projects)[![](https://img.shields.io/github/languages/code-size/vedanti-github/Spark_Projects.svg?style=plastic)](https://github.com/vedanti-github/Spark_Projects)[![](https://img.shields.io/github/languages/top/vedanti-github/Spark_Projects.svg?style=plastic)](https://github.com/vedanti-github/Spark_Projects)[![](https://img.shields.io/github/Follow/vedanti-github.svg?style=plastic)](https://github.com/vedanti-github)[![GitHub issues](https://img.shields.io/github/issues/vedanti-github/Spark_Projects.svg)](https://github.com/vedanti-github/Spark_Projects/issues) [![GitHub forks](https://img.shields.io/github/forks/vedanti-github/Spark_Projects.svg)](https://github.com/vedanti-github/Spark_Projects/network) [![GitHub stars](https://img.shields.io/github/stars/vedanti-github/Spark_Projects.svg)](https://github.com/vedanti-github/Spark_Projects/stargazers)
 [![](https://img.shields.io/badge/Followme-Github-green.svg)](https://github.com/vedanti-github)
 <br><br><br>
 <img height="27" src="https://img.shields.io/badge/Prediction using Supervised ML -Level  Beginner-green.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=red" />
@@ -14,9 +14,9 @@
 - You can use R, Python, SAS Enterprise Miner or any other tool.<br>
 - What will be predicted score if a student studies for 9.25 hrs/ day? <br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Supervised%20ML/student_scores%20-%20student_scores.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Supervised%20ML/Supervised%20ML%20.ipynb"> Prediction using Supervised ML</a>
 
 >Demo:
 <a href="https://youtu.be/yPUkaPVM2EY">Prediction using Supervised ML</a>
@@ -30,9 +30,9 @@
 represent it visually.<br>
 - Use R or Python or perform this task<br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Unsupervised%20ML/Iris.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Unsupervised%20ML/Unsupervised%20ML.ipynb"> Prediction using UnSupervised ML</a>
 
 > Demo:
 <a href="https://youtu.be/HV0tD0OQF5w">Prediction using Unsupervised ML</a>
@@ -49,9 +49,9 @@ make more profit.<br>
 - You can choose any of the tool of your choice<br>
 (Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Retail(Dataset).csv">Dataset.csv</a><br><br><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Retail(Dataset).csv">Dataset.csv</a><br><br><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Exploratory%20Data%20Analysis%20-%20Retail.ipynb"> Exploratory Data Analysis-Retail</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Exploratory%20Data%20Analysis%20-%20Retail/Exploratory%20Data%20Analysis%20-%20Retail.ipynb"> Exploratory Data Analysis-Retail</a>
 
 > Demo:
 <a href="https://youtu.be/tPzOUmbOKnQ">Exploratory Data Analysis-Retail</a>
@@ -66,9 +66,9 @@ make more profit.<br>
 predict the right class accordingly. <br>
 - Use R or Python or perform this task<br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/Iris27%20.csv">Dataset.csv</a><br><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Prediction%20using%20Decision%20Tree%20Algorithm/TSF%20task%234.ipynb">Prediction using DecisionTreeAlgorithm </a>
 
 > Demo:
 <a href="https://www.youtube.com/watch?v=UFl8yvJF4qk">Prediction using Decision Tree Algorithm</a>
@@ -84,9 +84,9 @@ predict the right class accordingly. <br>
 - You can choose any of the tool of your choice<br>
 (Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
 - Here is the dataset :
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/To%20explore%20Business%20Analytics/superstore.csv">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/To%20explore%20Business%20Analytics/Task-5.ipynb">To explore Business Analytics </a>
 
 > Demo:
 <a href="https://www.youtube.com/watch?v=n_asl4kNRcg">To explore Business Analytics</a>
@@ -104,7 +104,7 @@ predict the right class accordingly. <br>
 - Here is the dataset :
 <a href="https://bit.ly/2TK5Xn5">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Terrorism/Exploratory%20Data%20Analysis%20-%20Terrorism.ipynb">Exploratory Data Analysis - Terrorism</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Exploratory%20Data%20Analysis%20-%20Terrorism/Exploratory%20Data%20Analysis%20-%20Terrorism.ipynb">Exploratory Data Analysis - Terrorism</a>
 
 > Demo:
 <a href="https://youtu.be/a67W7Tl_g-8 ">Exploratory Data Analysis - Terrorism</a>
@@ -123,7 +123,7 @@ predict the right class accordingly. <br>
 - Here is the dataset :
 <a href="https://bit.ly/34SRn3b">Dataset.csv</a><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Exploratory%20Data%20Analysis%20-%20Sports/Exploratory%20Data%20Analysis%20-%20Sports.ipynb">Exploratory Data Analysis - Sports</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Exploratory%20Data%20Analysis%20-%20Sports/Exploratory%20Data%20Analysis%20-%20Sports.ipynb">Exploratory Data Analysis - Sports</a>
 
 > Demo:
 <a href="https://youtu.be/aXOHe1lsPBo">Exploratory Data Analysis - Sports</a>
@@ -146,7 +146,7 @@ while not changing the objective of the task.<br>
   - Download textual (news) data from https://bit.ly/36fFPI6
 </a><br>
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Stock%20Market%20Prediction%20using%20Numerical%20and%20Textual%20Analysis/Stock%20Market%20Prediction%20.ipynb">Stock Market Prediction using Numerical and Textual Analysis</a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Stock%20Market%20Prediction%20using%20Numerical%20and%20Textual%20Analysis/Stock%20Market%20Prediction%20.ipynb">Stock Market Prediction using Numerical and Textual Analysis</a>
 
 > Demo:
 <a href="https://youtu.be/4aQkjzaDQIg">Stock Market Prediction using Numerical and Textual Analysis</a>
@@ -165,7 +165,7 @@ basic as well as advanced charts <br>
 
   - Dataset: Daily updated .csv file on https://bit.ly/30d2gdi <br>  
 > Solution:
-<a href="https://github.com/vedanti-github/THE-SPARKS-FOUNDATION/blob/master/Timeline%20Analysis%20%20Covid-19/COVID19.ipynb">Timeline Analysis : Covid-19 </a>
+<a href="https://github.com/vedanti-github/Spark_Projects/blob/master/Timeline%20Analysis%20%20Covid-19/COVID19.ipynb">Timeline Analysis : Covid-19 </a>
 
 > Demo:
 <a href="">Timeline Analysis : Covid-19</a>
@@ -181,7 +181,7 @@ basic as well as advanced charts <br>
 <br />
 <hr />
 
-[youtube]: https://www.youtube.com/channel/UCIHj6mNCMnSnmWLHOxzIESw?view_as=subscriber
+[youtube]: https://www.youtube.com
 [gmail]: mailto:vedantiekre@gmail.com
 [linkedin]:https://www.linkedin.com/in/vedanti-ekre/
 [github]: https://github.com/vedanti-github/