From 4bec78a3b6328862e9e0ccdf45262fcc0367d363 Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Mon, 4 Oct 2021 11:10:49 +0530
Subject: [PATCH 1/6] Create PCA

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From 7b656da789eb3240013f7ec74059d4aa4e34d115 Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Mon, 4 Oct 2021 11:11:02 +0530
Subject: [PATCH 2/6] Delete PCA

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From 9be25f1f7eed1f0441ff4f24353c3415aef1c9ed Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Mon, 4 Oct 2021 11:12:30 +0530
Subject: [PATCH 3/6] Create PCA

---
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Principal Component Analysis(PCA)\n",
+    "\n",
+    "## PCA Review\n",
+    "\n",
+    "Make sure to watch the video lesson and theory presentation for a complete overview of the PCA!\n",
+    "Remember that PCA is just a transformation of your data, and it tries to find out which features explain the most variance in your data. For example:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src='PCA.png' />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 1: İmport Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 2: Get The Data\n",
+    "\n",
+    "Let's work with the cancer data set again since it had so many features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer = load_breast_cancer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sklearn.utils.Bunch"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(cancer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _breast_cancer_dataset:\n",
+      "\n",
+      "Breast cancer wisconsin (diagnostic) dataset\n",
+      "--------------------------------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 569\n",
+      "\n",
+      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
+      "\n",
+      "    :Attribute Information:\n",
+      "        - radius (mean of distances from center to points on the perimeter)\n",
+      "        - texture (standard deviation of gray-scale values)\n",
+      "        - perimeter\n",
+      "        - area\n",
+      "        - smoothness (local variation in radius lengths)\n",
+      "        - compactness (perimeter^2 / area - 1.0)\n",
+      "        - concavity (severity of concave portions of the contour)\n",
+      "        - concave points (number of concave portions of the contour)\n",
+      "        - symmetry\n",
+      "        - fractal dimension (\"coastline approximation\" - 1)\n",
+      "\n",
+      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
+      "        worst/largest values) of these features were computed for each image,\n",
+      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
+      "        10 is Radius SE, field 20 is Worst Radius.\n",
+      "\n",
+      "        - class:\n",
+      "                - WDBC-Malignant\n",
+      "                - WDBC-Benign\n",
+      "\n",
+      "    :Summary Statistics:\n",
+      "\n",
+      "    ===================================== ====== ======\n",
+      "                                           Min    Max\n",
+      "    ===================================== ====== ======\n",
+      "    radius (mean):                        6.981  28.11\n",
+      "    texture (mean):                       9.71   39.28\n",
+      "    perimeter (mean):                     43.79  188.5\n",
+      "    area (mean):                          143.5  2501.0\n",
+      "    smoothness (mean):                    0.053  0.163\n",
+      "    compactness (mean):                   0.019  0.345\n",
+      "    concavity (mean):                     0.0    0.427\n",
+      "    concave points (mean):                0.0    0.201\n",
+      "    symmetry (mean):                      0.106  0.304\n",
+      "    fractal dimension (mean):             0.05   0.097\n",
+      "    radius (standard error):              0.112  2.873\n",
+      "    texture (standard error):             0.36   4.885\n",
+      "    perimeter (standard error):           0.757  21.98\n",
+      "    area (standard error):                6.802  542.2\n",
+      "    smoothness (standard error):          0.002  0.031\n",
+      "    compactness (standard error):         0.002  0.135\n",
+      "    concavity (standard error):           0.0    0.396\n",
+      "    concave points (standard error):      0.0    0.053\n",
+      "    symmetry (standard error):            0.008  0.079\n",
+      "    fractal dimension (standard error):   0.001  0.03\n",
+      "    radius (worst):                       7.93   36.04\n",
+      "    texture (worst):                      12.02  49.54\n",
+      "    perimeter (worst):                    50.41  251.2\n",
+      "    area (worst):                         185.2  4254.0\n",
+      "    smoothness (worst):                   0.071  0.223\n",
+      "    compactness (worst):                  0.027  1.058\n",
+      "    concavity (worst):                    0.0    1.252\n",
+      "    concave points (worst):               0.0    0.291\n",
+      "    symmetry (worst):                     0.156  0.664\n",
+      "    fractal dimension (worst):            0.055  0.208\n",
+      "    ===================================== ====== ======\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "\n",
+      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
+      "\n",
+      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
+      "\n",
+      "    :Donor: Nick Street\n",
+      "\n",
+      "    :Date: November, 1995\n",
+      "\n",
+      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
+      "https://goo.gl/U2Uwz2\n",
+      "\n",
+      "Features are computed from a digitized image of a fine needle\n",
+      "aspirate (FNA) of a breast mass.  They describe\n",
+      "characteristics of the cell nuclei present in the image.\n",
+      "\n",
+      "Separating plane described above was obtained using\n",
+      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
+      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
+      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
+      "pp. 97-101, 1992], a classification method which uses linear\n",
+      "programming to construct a decision tree.  Relevant features\n",
+      "were selected using an exhaustive search in the space of 1-4\n",
+      "features and 1-3 separating planes.\n",
+      "\n",
+      "The actual linear program used to obtain the separating plane\n",
+      "in the 3-dimensional space is that described in:\n",
+      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
+      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
+      "Optimization Methods and Software 1, 1992, 23-34].\n",
+      "\n",
+      "This database is also available through the UW CS ftp server:\n",
+      "\n",
+      "ftp ftp.cs.wisc.edu\n",
+      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
+      "\n",
+      ".. topic:: References\n",
+      "\n",
+      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
+      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
+      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
+      "     San Jose, CA, 1993.\n",
+      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
+      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
+      "     July-August 1995.\n",
+      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
+      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
+      "     163-171.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cancer['DESCR'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
+    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst radius</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.3001</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.38</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.99</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.1974</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.57</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.2414</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.91</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.1980</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.54</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3        11.42         20.38           77.58      386.1          0.14250   \n",
+       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+       "\n",
+       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0           0.27760          0.3001              0.14710         0.2419   \n",
+       "1           0.07864          0.0869              0.07017         0.1812   \n",
+       "2           0.15990          0.1974              0.12790         0.2069   \n",
+       "3           0.28390          0.2414              0.10520         0.2597   \n",
+       "4           0.13280          0.1980              0.10430         0.1809   \n",
+       "\n",
+       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
+       "0                 0.07871  ...         25.38          17.33           184.60   \n",
+       "1                 0.05667  ...         24.99          23.41           158.80   \n",
+       "2                 0.05999  ...         23.57          25.53           152.50   \n",
+       "3                 0.09744  ...         14.91          26.50            98.87   \n",
+       "4                 0.05883  ...         22.54          16.67           152.20   \n",
+       "\n",
+       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
+       "0      2019.0            0.1622             0.6656           0.7119   \n",
+       "1      1956.0            0.1238             0.1866           0.2416   \n",
+       "2      1709.0            0.1444             0.4245           0.4504   \n",
+       "3       567.7            0.2098             0.8663           0.6869   \n",
+       "4      1575.0            0.1374             0.2050           0.4000   \n",
+       "\n",
+       "   worst concave points  worst symmetry  worst fractal dimension  \n",
+       "0                0.2654          0.4601                  0.11890  \n",
+       "1                0.1860          0.2750                  0.08902  \n",
+       "2                0.2430          0.3613                  0.08758  \n",
+       "3                0.2575          0.6638                  0.17300  \n",
+       "4                0.1625          0.2364                  0.07678  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 3: PCA Visualization\n",
+    "\n",
+    "As we have previously realized that it is difficult to visualize high dimensional data, we can use PCA to find the first two main components and visualize them with a single scatter plot in this new, two-dimensional space. Before we can do this though, we'll need to scale our data so that each feature has a single unit variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_data = scaler.transform(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Scikit Learn, PCA uses a process very similar to other preprocessing functions included with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
+    "\n",
+    "We can also specify how many components we want to keep when creating the PCA object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PCA(n_components=2)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.fit(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can transform this data to its first 2 principal components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_pca = pca.transform(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(569, 30)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(569, 2)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_pca.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! We've reduced 30 dimensions to just 2! Let's plot these two dimensions out!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Second Principal Component')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,6))\n",
+    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'],cmap='plasma')\n",
+    "plt.xlabel('First principal component')\n",
+    "plt.ylabel('Second Principal Component')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Obviously, we can easily separate these two classes using these two components."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 4: Interpreting the components \n",
+    "\n",
+    "Unfortunately, with this great power of dimensionality reduction, comes the cost of being able to easily understand what these components represent.\n",
+    "\n",
+    "The components correspond to combinations of the original features, the components themselves are stored as an attribute of the fitted PCA object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
+       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
+       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
+       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
+       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
+       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
+       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
+       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
+       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
+       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
+       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
+       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this numpy array of matrix, each row represents one main component and each column is related to the original elements. We can visualize this relationship with a heat map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_comp = pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c58c810610>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,6))\n",
+    "sns.heatmap(df_comp,cmap='plasma',)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This heatmap and color bar basically represent the relationship between the various feature and the main component itself.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Episode 5: Conclusion\n",
+    "\n",
+    "Hopefully this information is useful to you when dealing with high dimensional data!"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From e26a9f7e9e562f0b5582338c4852c77bebec3021 Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Mon, 4 Oct 2021 11:12:47 +0530
Subject: [PATCH 4/6] Add files via upload

---
 Prannov/Principal_Component_Analysis.ipynb | 809 +++++++++++++++++++++
 1 file changed, 809 insertions(+)
 create mode 100644 Prannov/Principal_Component_Analysis.ipynb

diff --git a/Prannov/Principal_Component_Analysis.ipynb b/Prannov/Principal_Component_Analysis.ipynb
new file mode 100644
index 0000000..3c42c97
--- /dev/null
+++ b/Prannov/Principal_Component_Analysis.ipynb
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Principal Component Analysis(PCA)\n",
+    "\n",
+    "## PCA Review\n",
+    "\n",
+    "Make sure to watch the video lesson and theory presentation for a complete overview of the PCA!\n",
+    "Remember that PCA is just a transformation of your data, and it tries to find out which features explain the most variance in your data. For example:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src='PCA.png' />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 1: İmport Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 2: Get The Data\n",
+    "\n",
+    "Let's work with the cancer data set again since it had so many features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer = load_breast_cancer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sklearn.utils.Bunch"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(cancer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _breast_cancer_dataset:\n",
+      "\n",
+      "Breast cancer wisconsin (diagnostic) dataset\n",
+      "--------------------------------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 569\n",
+      "\n",
+      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
+      "\n",
+      "    :Attribute Information:\n",
+      "        - radius (mean of distances from center to points on the perimeter)\n",
+      "        - texture (standard deviation of gray-scale values)\n",
+      "        - perimeter\n",
+      "        - area\n",
+      "        - smoothness (local variation in radius lengths)\n",
+      "        - compactness (perimeter^2 / area - 1.0)\n",
+      "        - concavity (severity of concave portions of the contour)\n",
+      "        - concave points (number of concave portions of the contour)\n",
+      "        - symmetry\n",
+      "        - fractal dimension (\"coastline approximation\" - 1)\n",
+      "\n",
+      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
+      "        worst/largest values) of these features were computed for each image,\n",
+      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
+      "        10 is Radius SE, field 20 is Worst Radius.\n",
+      "\n",
+      "        - class:\n",
+      "                - WDBC-Malignant\n",
+      "                - WDBC-Benign\n",
+      "\n",
+      "    :Summary Statistics:\n",
+      "\n",
+      "    ===================================== ====== ======\n",
+      "                                           Min    Max\n",
+      "    ===================================== ====== ======\n",
+      "    radius (mean):                        6.981  28.11\n",
+      "    texture (mean):                       9.71   39.28\n",
+      "    perimeter (mean):                     43.79  188.5\n",
+      "    area (mean):                          143.5  2501.0\n",
+      "    smoothness (mean):                    0.053  0.163\n",
+      "    compactness (mean):                   0.019  0.345\n",
+      "    concavity (mean):                     0.0    0.427\n",
+      "    concave points (mean):                0.0    0.201\n",
+      "    symmetry (mean):                      0.106  0.304\n",
+      "    fractal dimension (mean):             0.05   0.097\n",
+      "    radius (standard error):              0.112  2.873\n",
+      "    texture (standard error):             0.36   4.885\n",
+      "    perimeter (standard error):           0.757  21.98\n",
+      "    area (standard error):                6.802  542.2\n",
+      "    smoothness (standard error):          0.002  0.031\n",
+      "    compactness (standard error):         0.002  0.135\n",
+      "    concavity (standard error):           0.0    0.396\n",
+      "    concave points (standard error):      0.0    0.053\n",
+      "    symmetry (standard error):            0.008  0.079\n",
+      "    fractal dimension (standard error):   0.001  0.03\n",
+      "    radius (worst):                       7.93   36.04\n",
+      "    texture (worst):                      12.02  49.54\n",
+      "    perimeter (worst):                    50.41  251.2\n",
+      "    area (worst):                         185.2  4254.0\n",
+      "    smoothness (worst):                   0.071  0.223\n",
+      "    compactness (worst):                  0.027  1.058\n",
+      "    concavity (worst):                    0.0    1.252\n",
+      "    concave points (worst):               0.0    0.291\n",
+      "    symmetry (worst):                     0.156  0.664\n",
+      "    fractal dimension (worst):            0.055  0.208\n",
+      "    ===================================== ====== ======\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "\n",
+      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
+      "\n",
+      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
+      "\n",
+      "    :Donor: Nick Street\n",
+      "\n",
+      "    :Date: November, 1995\n",
+      "\n",
+      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
+      "https://goo.gl/U2Uwz2\n",
+      "\n",
+      "Features are computed from a digitized image of a fine needle\n",
+      "aspirate (FNA) of a breast mass.  They describe\n",
+      "characteristics of the cell nuclei present in the image.\n",
+      "\n",
+      "Separating plane described above was obtained using\n",
+      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
+      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
+      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
+      "pp. 97-101, 1992], a classification method which uses linear\n",
+      "programming to construct a decision tree.  Relevant features\n",
+      "were selected using an exhaustive search in the space of 1-4\n",
+      "features and 1-3 separating planes.\n",
+      "\n",
+      "The actual linear program used to obtain the separating plane\n",
+      "in the 3-dimensional space is that described in:\n",
+      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
+      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
+      "Optimization Methods and Software 1, 1992, 23-34].\n",
+      "\n",
+      "This database is also available through the UW CS ftp server:\n",
+      "\n",
+      "ftp ftp.cs.wisc.edu\n",
+      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
+      "\n",
+      ".. topic:: References\n",
+      "\n",
+      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
+      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
+      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
+      "     San Jose, CA, 1993.\n",
+      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
+      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
+      "     July-August 1995.\n",
+      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
+      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
+      "     163-171.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cancer['DESCR'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
+    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst radius</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.3001</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.38</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.99</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.1974</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.57</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.2414</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.91</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.1980</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.54</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3        11.42         20.38           77.58      386.1          0.14250   \n",
+       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+       "\n",
+       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0           0.27760          0.3001              0.14710         0.2419   \n",
+       "1           0.07864          0.0869              0.07017         0.1812   \n",
+       "2           0.15990          0.1974              0.12790         0.2069   \n",
+       "3           0.28390          0.2414              0.10520         0.2597   \n",
+       "4           0.13280          0.1980              0.10430         0.1809   \n",
+       "\n",
+       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
+       "0                 0.07871  ...         25.38          17.33           184.60   \n",
+       "1                 0.05667  ...         24.99          23.41           158.80   \n",
+       "2                 0.05999  ...         23.57          25.53           152.50   \n",
+       "3                 0.09744  ...         14.91          26.50            98.87   \n",
+       "4                 0.05883  ...         22.54          16.67           152.20   \n",
+       "\n",
+       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
+       "0      2019.0            0.1622             0.6656           0.7119   \n",
+       "1      1956.0            0.1238             0.1866           0.2416   \n",
+       "2      1709.0            0.1444             0.4245           0.4504   \n",
+       "3       567.7            0.2098             0.8663           0.6869   \n",
+       "4      1575.0            0.1374             0.2050           0.4000   \n",
+       "\n",
+       "   worst concave points  worst symmetry  worst fractal dimension  \n",
+       "0                0.2654          0.4601                  0.11890  \n",
+       "1                0.1860          0.2750                  0.08902  \n",
+       "2                0.2430          0.3613                  0.08758  \n",
+       "3                0.2575          0.6638                  0.17300  \n",
+       "4                0.1625          0.2364                  0.07678  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 3: PCA Visualization\n",
+    "\n",
+    "As we have previously realized that it is difficult to visualize high dimensional data, we can use PCA to find the first two main components and visualize them with a single scatter plot in this new, two-dimensional space. Before we can do this though, we'll need to scale our data so that each feature has a single unit variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_data = scaler.transform(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Scikit Learn, PCA uses a process very similar to other preprocessing functions included with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
+    "\n",
+    "We can also specify how many components we want to keep when creating the PCA object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PCA(n_components=2)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.fit(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can transform this data to its first 2 principal components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_pca = pca.transform(scaled_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(569, 30)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(569, 2)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_pca.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! We've reduced 30 dimensions to just 2! Let's plot these two dimensions out!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Second Principal Component')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,6))\n",
+    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'],cmap='plasma')\n",
+    "plt.xlabel('First principal component')\n",
+    "plt.ylabel('Second Principal Component')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Obviously, we can easily separate these two classes using these two components."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Episode 4: Interpreting the components \n",
+    "\n",
+    "Unfortunately, with this great power of dimensionality reduction, comes the cost of being able to easily understand what these components represent.\n",
+    "\n",
+    "The components correspond to combinations of the original features, the components themselves are stored as an attribute of the fitted PCA object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
+       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
+       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
+       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
+       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
+       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
+       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
+       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
+       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
+       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
+       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
+       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this numpy array of matrix, each row represents one main component and each column is related to the original elements. We can visualize this relationship with a heat map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_comp = pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c58c810610>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,6))\n",
+    "sns.heatmap(df_comp,cmap='plasma',)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This heatmap and color bar basically represent the relationship between the various feature and the main component itself.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Episode 5: Conclusion\n",
+    "\n",
+    "Hopefully this information is useful to you when dealing with high dimensional data!"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 4b870e34aafa374c3d17241a2b55aee2717ef587 Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Mon, 4 Oct 2021 11:13:02 +0530
Subject: [PATCH 5/6] Delete PCA

---
 Prannov/PCA | 809 ----------------------------------------------------
 1 file changed, 809 deletions(-)
 delete mode 100644 Prannov/PCA

diff --git a/Prannov/PCA b/Prannov/PCA
deleted file mode 100644
index 3c42c97..0000000
--- a/Prannov/PCA
+++ /dev/null
@@ -1,809 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Principal Component Analysis(PCA)\n",
-    "\n",
-    "## PCA Review\n",
-    "\n",
-    "Make sure to watch the video lesson and theory presentation for a complete overview of the PCA!\n",
-    "Remember that PCA is just a transformation of your data, and it tries to find out which features explain the most variance in your data. For example:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src='PCA.png' />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Episode 1: İmport Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Episode 2: Get The Data\n",
-    "\n",
-    "Let's work with the cancer data set again since it had so many features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_breast_cancer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cancer = load_breast_cancer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sklearn.utils.Bunch"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(cancer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cancer.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".. _breast_cancer_dataset:\n",
-      "\n",
-      "Breast cancer wisconsin (diagnostic) dataset\n",
-      "--------------------------------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      "    :Number of Instances: 569\n",
-      "\n",
-      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
-      "\n",
-      "    :Attribute Information:\n",
-      "        - radius (mean of distances from center to points on the perimeter)\n",
-      "        - texture (standard deviation of gray-scale values)\n",
-      "        - perimeter\n",
-      "        - area\n",
-      "        - smoothness (local variation in radius lengths)\n",
-      "        - compactness (perimeter^2 / area - 1.0)\n",
-      "        - concavity (severity of concave portions of the contour)\n",
-      "        - concave points (number of concave portions of the contour)\n",
-      "        - symmetry\n",
-      "        - fractal dimension (\"coastline approximation\" - 1)\n",
-      "\n",
-      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
-      "        worst/largest values) of these features were computed for each image,\n",
-      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
-      "        10 is Radius SE, field 20 is Worst Radius.\n",
-      "\n",
-      "        - class:\n",
-      "                - WDBC-Malignant\n",
-      "                - WDBC-Benign\n",
-      "\n",
-      "    :Summary Statistics:\n",
-      "\n",
-      "    ===================================== ====== ======\n",
-      "                                           Min    Max\n",
-      "    ===================================== ====== ======\n",
-      "    radius (mean):                        6.981  28.11\n",
-      "    texture (mean):                       9.71   39.28\n",
-      "    perimeter (mean):                     43.79  188.5\n",
-      "    area (mean):                          143.5  2501.0\n",
-      "    smoothness (mean):                    0.053  0.163\n",
-      "    compactness (mean):                   0.019  0.345\n",
-      "    concavity (mean):                     0.0    0.427\n",
-      "    concave points (mean):                0.0    0.201\n",
-      "    symmetry (mean):                      0.106  0.304\n",
-      "    fractal dimension (mean):             0.05   0.097\n",
-      "    radius (standard error):              0.112  2.873\n",
-      "    texture (standard error):             0.36   4.885\n",
-      "    perimeter (standard error):           0.757  21.98\n",
-      "    area (standard error):                6.802  542.2\n",
-      "    smoothness (standard error):          0.002  0.031\n",
-      "    compactness (standard error):         0.002  0.135\n",
-      "    concavity (standard error):           0.0    0.396\n",
-      "    concave points (standard error):      0.0    0.053\n",
-      "    symmetry (standard error):            0.008  0.079\n",
-      "    fractal dimension (standard error):   0.001  0.03\n",
-      "    radius (worst):                       7.93   36.04\n",
-      "    texture (worst):                      12.02  49.54\n",
-      "    perimeter (worst):                    50.41  251.2\n",
-      "    area (worst):                         185.2  4254.0\n",
-      "    smoothness (worst):                   0.071  0.223\n",
-      "    compactness (worst):                  0.027  1.058\n",
-      "    concavity (worst):                    0.0    1.252\n",
-      "    concave points (worst):               0.0    0.291\n",
-      "    symmetry (worst):                     0.156  0.664\n",
-      "    fractal dimension (worst):            0.055  0.208\n",
-      "    ===================================== ====== ======\n",
-      "\n",
-      "    :Missing Attribute Values: None\n",
-      "\n",
-      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
-      "\n",
-      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
-      "\n",
-      "    :Donor: Nick Street\n",
-      "\n",
-      "    :Date: November, 1995\n",
-      "\n",
-      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
-      "https://goo.gl/U2Uwz2\n",
-      "\n",
-      "Features are computed from a digitized image of a fine needle\n",
-      "aspirate (FNA) of a breast mass.  They describe\n",
-      "characteristics of the cell nuclei present in the image.\n",
-      "\n",
-      "Separating plane described above was obtained using\n",
-      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
-      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
-      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
-      "pp. 97-101, 1992], a classification method which uses linear\n",
-      "programming to construct a decision tree.  Relevant features\n",
-      "were selected using an exhaustive search in the space of 1-4\n",
-      "features and 1-3 separating planes.\n",
-      "\n",
-      "The actual linear program used to obtain the separating plane\n",
-      "in the 3-dimensional space is that described in:\n",
-      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
-      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
-      "Optimization Methods and Software 1, 1992, 23-34].\n",
-      "\n",
-      "This database is also available through the UW CS ftp server:\n",
-      "\n",
-      "ftp ftp.cs.wisc.edu\n",
-      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
-      "\n",
-      ".. topic:: References\n",
-      "\n",
-      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
-      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
-      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
-      "     San Jose, CA, 1993.\n",
-      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
-      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
-      "     July-August 1995.\n",
-      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
-      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
-      "     163-171.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cancer['DESCR'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
-    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>mean radius</th>\n",
-       "      <th>mean texture</th>\n",
-       "      <th>mean perimeter</th>\n",
-       "      <th>mean area</th>\n",
-       "      <th>mean smoothness</th>\n",
-       "      <th>mean compactness</th>\n",
-       "      <th>mean concavity</th>\n",
-       "      <th>mean concave points</th>\n",
-       "      <th>mean symmetry</th>\n",
-       "      <th>mean fractal dimension</th>\n",
-       "      <th>...</th>\n",
-       "      <th>worst radius</th>\n",
-       "      <th>worst texture</th>\n",
-       "      <th>worst perimeter</th>\n",
-       "      <th>worst area</th>\n",
-       "      <th>worst smoothness</th>\n",
-       "      <th>worst compactness</th>\n",
-       "      <th>worst concavity</th>\n",
-       "      <th>worst concave points</th>\n",
-       "      <th>worst symmetry</th>\n",
-       "      <th>worst fractal dimension</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17.99</td>\n",
-       "      <td>10.38</td>\n",
-       "      <td>122.80</td>\n",
-       "      <td>1001.0</td>\n",
-       "      <td>0.11840</td>\n",
-       "      <td>0.27760</td>\n",
-       "      <td>0.3001</td>\n",
-       "      <td>0.14710</td>\n",
-       "      <td>0.2419</td>\n",
-       "      <td>0.07871</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.38</td>\n",
-       "      <td>17.33</td>\n",
-       "      <td>184.60</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>0.1622</td>\n",
-       "      <td>0.6656</td>\n",
-       "      <td>0.7119</td>\n",
-       "      <td>0.2654</td>\n",
-       "      <td>0.4601</td>\n",
-       "      <td>0.11890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20.57</td>\n",
-       "      <td>17.77</td>\n",
-       "      <td>132.90</td>\n",
-       "      <td>1326.0</td>\n",
-       "      <td>0.08474</td>\n",
-       "      <td>0.07864</td>\n",
-       "      <td>0.0869</td>\n",
-       "      <td>0.07017</td>\n",
-       "      <td>0.1812</td>\n",
-       "      <td>0.05667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>24.99</td>\n",
-       "      <td>23.41</td>\n",
-       "      <td>158.80</td>\n",
-       "      <td>1956.0</td>\n",
-       "      <td>0.1238</td>\n",
-       "      <td>0.1866</td>\n",
-       "      <td>0.2416</td>\n",
-       "      <td>0.1860</td>\n",
-       "      <td>0.2750</td>\n",
-       "      <td>0.08902</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.69</td>\n",
-       "      <td>21.25</td>\n",
-       "      <td>130.00</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>0.10960</td>\n",
-       "      <td>0.15990</td>\n",
-       "      <td>0.1974</td>\n",
-       "      <td>0.12790</td>\n",
-       "      <td>0.2069</td>\n",
-       "      <td>0.05999</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.57</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>152.50</td>\n",
-       "      <td>1709.0</td>\n",
-       "      <td>0.1444</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.4504</td>\n",
-       "      <td>0.2430</td>\n",
-       "      <td>0.3613</td>\n",
-       "      <td>0.08758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.42</td>\n",
-       "      <td>20.38</td>\n",
-       "      <td>77.58</td>\n",
-       "      <td>386.1</td>\n",
-       "      <td>0.14250</td>\n",
-       "      <td>0.28390</td>\n",
-       "      <td>0.2414</td>\n",
-       "      <td>0.10520</td>\n",
-       "      <td>0.2597</td>\n",
-       "      <td>0.09744</td>\n",
-       "      <td>...</td>\n",
-       "      <td>14.91</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>98.87</td>\n",
-       "      <td>567.7</td>\n",
-       "      <td>0.2098</td>\n",
-       "      <td>0.8663</td>\n",
-       "      <td>0.6869</td>\n",
-       "      <td>0.2575</td>\n",
-       "      <td>0.6638</td>\n",
-       "      <td>0.17300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.29</td>\n",
-       "      <td>14.34</td>\n",
-       "      <td>135.10</td>\n",
-       "      <td>1297.0</td>\n",
-       "      <td>0.10030</td>\n",
-       "      <td>0.13280</td>\n",
-       "      <td>0.1980</td>\n",
-       "      <td>0.10430</td>\n",
-       "      <td>0.1809</td>\n",
-       "      <td>0.05883</td>\n",
-       "      <td>...</td>\n",
-       "      <td>22.54</td>\n",
-       "      <td>16.67</td>\n",
-       "      <td>152.20</td>\n",
-       "      <td>1575.0</td>\n",
-       "      <td>0.1374</td>\n",
-       "      <td>0.2050</td>\n",
-       "      <td>0.4000</td>\n",
-       "      <td>0.1625</td>\n",
-       "      <td>0.2364</td>\n",
-       "      <td>0.07678</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 30 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
-       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
-       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
-       "3        11.42         20.38           77.58      386.1          0.14250   \n",
-       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
-       "\n",
-       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-       "0           0.27760          0.3001              0.14710         0.2419   \n",
-       "1           0.07864          0.0869              0.07017         0.1812   \n",
-       "2           0.15990          0.1974              0.12790         0.2069   \n",
-       "3           0.28390          0.2414              0.10520         0.2597   \n",
-       "4           0.13280          0.1980              0.10430         0.1809   \n",
-       "\n",
-       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
-       "0                 0.07871  ...         25.38          17.33           184.60   \n",
-       "1                 0.05667  ...         24.99          23.41           158.80   \n",
-       "2                 0.05999  ...         23.57          25.53           152.50   \n",
-       "3                 0.09744  ...         14.91          26.50            98.87   \n",
-       "4                 0.05883  ...         22.54          16.67           152.20   \n",
-       "\n",
-       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
-       "0      2019.0            0.1622             0.6656           0.7119   \n",
-       "1      1956.0            0.1238             0.1866           0.2416   \n",
-       "2      1709.0            0.1444             0.4245           0.4504   \n",
-       "3       567.7            0.2098             0.8663           0.6869   \n",
-       "4      1575.0            0.1374             0.2050           0.4000   \n",
-       "\n",
-       "   worst concave points  worst symmetry  worst fractal dimension  \n",
-       "0                0.2654          0.4601                  0.11890  \n",
-       "1                0.1860          0.2750                  0.08902  \n",
-       "2                0.2430          0.3613                  0.08758  \n",
-       "3                0.2575          0.6638                  0.17300  \n",
-       "4                0.1625          0.2364                  0.07678  \n",
-       "\n",
-       "[5 rows x 30 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Episode 3: PCA Visualization\n",
-    "\n",
-    "As we have previously realized that it is difficult to visualize high dimensional data, we can use PCA to find the first two main components and visualize them with a single scatter plot in this new, two-dimensional space. Before we can do this though, we'll need to scale our data so that each feature has a single unit variance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "StandardScaler()"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "scaler.fit(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaled_data = scaler.transform(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With Scikit Learn, PCA uses a process very similar to other preprocessing functions included with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
-    "\n",
-    "We can also specify how many components we want to keep when creating the PCA object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.decomposition import PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca = PCA(n_components=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PCA(n_components=2)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca.fit(scaled_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can transform this data to its first 2 principal components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_pca = pca.transform(scaled_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(569, 30)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaled_data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(569, 2)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_pca.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great! We've reduced 30 dimensions to just 2! Let's plot these two dimensions out!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Second Principal Component')"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8,6))\n",
-    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'],cmap='plasma')\n",
-    "plt.xlabel('First principal component')\n",
-    "plt.ylabel('Second Principal Component')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Obviously, we can easily separate these two classes using these two components."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Episode 4: Interpreting the components \n",
-    "\n",
-    "Unfortunately, with this great power of dimensionality reduction, comes the cost of being able to easily understand what these components represent.\n",
-    "\n",
-    "The components correspond to combinations of the original features, the components themselves are stored as an attribute of the fitted PCA object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
-       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
-       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
-       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
-       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
-       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
-       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
-       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
-       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
-       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
-       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
-       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca.components_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this numpy array of matrix, each row represents one main component and each column is related to the original elements. We can visualize this relationship with a heat map:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_comp = pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c58c810610>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12,6))\n",
-    "sns.heatmap(df_comp,cmap='plasma',)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This heatmap and color bar basically represent the relationship between the various feature and the main component itself.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Episode 5: Conclusion\n",
-    "\n",
-    "Hopefully this information is useful to you when dealing with high dimensional data!"
-   ]
-  }
- ],
- "metadata": {
-  "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": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

From f2aacbbe5033845ba5dae7ec1defcb6a00aac8b7 Mon Sep 17 00:00:00 2001
From: Prannov Jamadagni <86357036+Prannov@users.noreply.github.com>
Date: Tue, 5 Oct 2021 15:27:13 +0530
Subject: [PATCH 6/6] Add files via upload

---
 Prannov/slnb.ipynb | 94 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 Prannov/slnb.ipynb

diff --git a/Prannov/slnb.ipynb b/Prannov/slnb.ipynb
new file mode 100644
index 0000000..1ee172d
--- /dev/null
+++ b/Prannov/slnb.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X = np.array([[-1,-1], [-2,-1], [-4,-4], [-8,-2], [1,2], [2,5], [4,6], [5,2], [-2,3], [-4,1], [-9,8], [-1,1], \n",
+    "              [3,-2], [4,-1], [7,-6], [1,-1], [0,0]])\n",
+    "Y = np.array([3,3,3,3,1,1,1,1,2,2,2,2,4,4,4,4,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GaussianNB()\n",
+      "[0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "clf = GaussianNB()\n",
+    "clf_fit = clf.fit(X, Y)\n",
+    "print(clf_fit)\n",
+    "pred = clf.predict([[0,0]])\n",
+    "print(pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import svm\n",
+    "\n",
+    "clf1 = svm.SVC()\n",
+    "clf1.fit(X,Y)\n",
+    "pred1 = clf1.predict([[3,3]])\n",
+    "pred1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}