diff --git a/citrus.csv b/citrus.csv
new file mode 100644
index 0000000..e4341d7
--- /dev/null
+++ b/citrus.csv
@@ -0,0 +1,10001 @@
+name,diameter,weight,red,green,blue
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+grapefruit,13.84,234.53,152,73,6
+grapefruit,13.84,234.53,142,59,29
+grapefruit,13.85,234.62,131,64,23
+grapefruit,13.85,234.7,167,76,23
+grapefruit,13.85,234.73,139,69,33
+grapefruit,13.86,234.8,156,66,2
+grapefruit,13.86,234.82,154,86,18
+grapefruit,13.86,234.87,145,65,14
+grapefruit,13.87,234.88,162,60,13
+grapefruit,13.87,235.0,145,90,13
+grapefruit,13.87,235.2,160,66,36
+grapefruit,13.87,235.22,144,80,8
+grapefruit,13.88,235.3,168,73,8
+grapefruit,13.89,235.31,148,67,7
+grapefruit,13.89,235.46,154,79,18
+grapefruit,13.9,235.48,151,57,17
+grapefruit,13.91,235.49,158,64,9
+grapefruit,13.91,235.77,141,57,29
+grapefruit,13.91,235.89,149,63,29
+grapefruit,13.92,235.9,170,74,5
+grapefruit,13.92,235.91,149,77,28
+grapefruit,13.92,235.95,167,83,7
+grapefruit,13.92,235.98,140,70,29
+grapefruit,13.93,236.09,156,73,27
+grapefruit,13.93,236.09,157,80,2
+grapefruit,13.94,236.16,139,63,28
+grapefruit,13.94,236.22,149,78,18
+grapefruit,13.97,236.29,141,57,11
+grapefruit,13.98,236.38,126,75,6
+grapefruit,13.98,236.42,160,78,25
+grapefruit,13.98,236.46,147,56,14
+grapefruit,13.99,236.66,154,66,10
+grapefruit,13.99,236.84,155,74,11
+grapefruit,13.99,236.94,146,74,2
+grapefruit,13.99,237.05,151,90,2
+grapefruit,14.0,237.05,160,66,24
+grapefruit,14.01,237.23,163,74,3
+grapefruit,14.03,237.25,159,71,35
+grapefruit,14.03,237.42,170,70,24
+grapefruit,14.03,237.44,139,64,13
+grapefruit,14.03,237.55,166,63,11
+grapefruit,14.04,237.57,148,73,27
+grapefruit,14.04,237.67,167,64,16
+grapefruit,14.05,237.82,153,57,16
+grapefruit,14.05,237.87,149,77,2
+grapefruit,14.06,238.0,159,88,7
+grapefruit,14.06,238.21,165,61,18
+grapefruit,14.06,238.28,140,67,22
+grapefruit,14.07,238.3,146,49,22
+grapefruit,14.07,238.31,148,70,7
+grapefruit,14.07,238.36,148,54,21
+grapefruit,14.09,238.52,157,59,2
+grapefruit,14.09,238.54,151,49,22
+grapefruit,14.09,238.57,167,84,22
+grapefruit,14.09,238.72,144,69,21
+grapefruit,14.09,238.76,152,78,43
+grapefruit,14.09,238.78,148,77,6
+grapefruit,14.1,238.96,164,63,4
+grapefruit,14.11,239.12,156,70,17
+grapefruit,14.12,239.22,170,76,16
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+grapefruit,14.19,240.19,159,66,14
+grapefruit,14.19,240.23,165,72,2
+grapefruit,14.2,240.36,135,66,22
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+grapefruit,14.2,240.44,150,76,6
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+grapefruit,14.22,240.56,145,65,21
+grapefruit,14.22,240.65,145,67,30
+grapefruit,14.25,240.74,159,62,5
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+grapefruit,14.28,240.77,160,68,24
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+grapefruit,14.29,240.98,140,67,5
+grapefruit,14.3,241.02,143,81,27
+grapefruit,14.3,241.13,151,74,8
+grapefruit,14.31,241.33,146,69,29
+grapefruit,14.32,241.55,137,65,19
+grapefruit,14.32,241.74,157,76,3
+grapefruit,14.32,241.77,157,72,16
+grapefruit,14.33,241.89,147,62,27
+grapefruit,14.34,241.93,156,80,2
+grapefruit,14.34,242.12,148,71,15
+grapefruit,14.34,242.13,160,67,10
+grapefruit,14.35,242.2,148,75,22
+grapefruit,14.35,242.4,134,65,24
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+grapefruit,14.36,242.59,155,63,11
+grapefruit,14.36,242.65,140,72,2
+grapefruit,14.37,242.67,156,70,38
+grapefruit,14.37,242.68,160,72,21
+grapefruit,14.38,242.7,139,60,14
+grapefruit,14.43,242.79,143,74,20
+grapefruit,14.5,242.81,141,71,22
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+grapefruit,14.51,242.96,147,85,19
+grapefruit,14.54,243.08,143,62,15
+grapefruit,14.56,243.24,156,76,8
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+grapefruit,14.58,243.93,141,64,20
+grapefruit,14.59,243.93,146,78,23
+grapefruit,14.59,244.06,132,64,12
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+grapefruit,14.63,244.68,172,79,28
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+grapefruit,14.67,244.78,163,62,2
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+grapefruit,15.41,254.67,148,68,7
+grapefruit,15.59,256.5,168,82,20
+grapefruit,15.92,260.14,142,72,11
+grapefruit,16.45,261.51,152,74,2
diff --git a/colab.ipynb b/colab.ipynb
index 8557fcc..538a0ba 100644
--- a/colab.ipynb
+++ b/colab.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 194,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -117,6 +117,14 @@
     "Before we dive in to looking closely at the data, let's think about the problem space and the dataset. Consider the questions below."
    ]
   },
+  {
+   "source": [
+    "Lemons,Limes,Oranges,Grapefruit,  \n",
+    "Weight,color,diameter"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -129,16 +137,6 @@
     "Is this problem actually a good fit for machine learning? Why or why not?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "QEECNP0z4Eqz"
-   },
-   "source": [
-    "##### **Student Solution**"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -146,17 +144,7 @@
     "id": "BNcJPqVF6UNi"
    },
    "source": [
-    "*Please Put Your Answer Here*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PZsXA8-xXAo5"
-   },
-   "source": [
-    "---"
+    ">The citrus problem is a good fit for machine learning. It's based on classifying fruits based on a set of inputs, which include weight, color, and maximum diameter. The output would label to lemons,limes,oranges, or grapefruit."
    ]
   },
   {
@@ -178,17 +166,7 @@
     "id": "0P_GjcKE4Nil"
    },
    "source": [
-    "##### **Student Solution**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "XzqOPKOb6_A2"
-   },
-   "source": [
-    "*Please Put Your Answer Here*"
+    ">There could exist measurement bias due to underlying problems with the accuracy of the data and how is measured or assessed. Precision and accuracy would be sources to consider when trying to collect weight and max diameter data"
    ]
   },
   {
@@ -221,16 +199,6 @@
     "Justify the inputs and the output actions for the system. What are the trade-offs?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "M6wlYr_74KMT"
-   },
-   "source": [
-    "##### **Student Solution**"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -238,7 +206,7 @@
     "id": "nEO2I08VcgfR"
    },
    "source": [
-    "*Please Put Your Answer Here*\n"
+    ">The input for this kind of sytem would ideally be all the fruit that Cindy receives. When creating algorithms for situations like these, it is important to consider a wide range of data. Limiting the data inputs to just a specfic set of fruits will not be as representative compared to a large set of fruits.\n"
    ]
   },
   {
@@ -305,15 +273,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1DCns7DaatrV"
-   },
-   "outputs": [],
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "chmod: kaggle.json: No such file or directory\n"
+     ]
+    }
+   ],
    "source": [
-    "!KAGGLE_CONFIG_DIR=`pwd` kaggle datasets download joshmcadams/oranges-vs-grapefruit\n",
+    "! chmod 600 kaggle.json && (ls ~/.kaggle 2>/dev/null || mkdir ~/.kaggle) && mv kaggle.json ~/.kaggle/ && echo 'Done'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "oranges-vs-grapefruit.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
+      "AMLI - Classification- Binary.pdf colab.ipynb\n",
+      "README.md                         oranges-vs-grapefruit.zip\n",
+      "citrus.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kaggle datasets download joshmcadams/oranges-vs-grapefruit\n",
     "!ls"
    ]
   },
@@ -329,13 +321,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 197,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "-lVK5qcenhL6"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Archive:  oranges-vs-grapefruit.zip\n",
+      "  inflating: citrus.csv              \n",
+      "AMLI - Classification- Binary.pdf colab.ipynb\n",
+      "README.md                         oranges-vs-grapefruit.zip\n",
+      "citrus.csv\n"
+     ]
+    }
+   ],
    "source": [
     "!unzip -o oranges-vs-grapefruit.zip\n",
     "!ls"
@@ -376,13 +380,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 198,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "E0tP0yHJ7orM"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "            name  diameter  weight  red  green  blue\n",
+       "4436      orange      9.98  175.92  159     73    11\n",
+       "3829      orange      9.38  166.49  149     75     2\n",
+       "2434      orange      8.43  152.07  155     79    11\n",
+       "4449      orange      9.99  176.13  161     86     2\n",
+       "43        orange      5.40  108.18  148     81     2\n",
+       "9961  grapefruit     14.37  242.67  156     70    38\n",
+       "4509      orange     10.09  177.14  153     90     3\n",
+       "5114  grapefruit      9.10  158.51  149     82    31\n",
+       "9374  grapefruit     12.90  219.27  151     64    24\n",
+       "3273      orange      8.96  160.25  159     95    12"
+      ],
+      "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>name</th>\n      <th>diameter</th>\n      <th>weight</th>\n      <th>red</th>\n      <th>green</th>\n      <th>blue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4436</th>\n      <td>orange</td>\n      <td>9.98</td>\n      <td>175.92</td>\n      <td>159</td>\n      <td>73</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3829</th>\n      <td>orange</td>\n      <td>9.38</td>\n      <td>166.49</td>\n      <td>149</td>\n      <td>75</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2434</th>\n      <td>orange</td>\n      <td>8.43</td>\n      <td>152.07</td>\n      <td>155</td>\n      <td>79</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4449</th>\n      <td>orange</td>\n      <td>9.99</td>\n      <td>176.13</td>\n      <td>161</td>\n      <td>86</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>orange</td>\n      <td>5.40</td>\n      <td>108.18</td>\n      <td>148</td>\n      <td>81</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9961</th>\n      <td>grapefruit</td>\n      <td>14.37</td>\n      <td>242.67</td>\n      <td>156</td>\n      <td>70</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>4509</th>\n      <td>orange</td>\n      <td>10.09</td>\n      <td>177.14</td>\n      <td>153</td>\n      <td>90</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5114</th>\n      <td>grapefruit</td>\n      <td>9.10</td>\n      <td>158.51</td>\n      <td>149</td>\n      <td>82</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>9374</th>\n      <td>grapefruit</td>\n      <td>12.90</td>\n      <td>219.27</td>\n      <td>151</td>\n      <td>64</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>3273</th>\n      <td>orange</td>\n      <td>8.96</td>\n      <td>160.25</td>\n      <td>159</td>\n      <td>95</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 198
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -426,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 199,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -434,7 +460,8 @@
    },
    "outputs": [],
    "source": [
-    "# Your Solution Goes Here"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -447,6 +474,28 @@
     "---"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.hist(citrus_df['name'])\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -490,13 +539,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 201,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "todQOhCQAayf"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           diameter        weight           red         green          blue\n",
+       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000\n",
+       "mean       9.975685    175.050792    153.847800     76.010600     11.363200\n",
+       "std        1.947844     29.212119     10.432954     11.708433      9.061275\n",
+       "min        2.960000     86.760000    115.000000     31.000000      2.000000\n",
+       "25%        8.460000    152.220000    147.000000     68.000000      2.000000\n",
+       "50%        9.980000    174.985000    154.000000     76.000000     10.000000\n",
+       "75%       11.480000    197.722500    161.000000     84.000000     17.000000\n",
+       "max       16.450000    261.510000    192.000000    116.000000     56.000000"
+      ],
+      "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>diameter</th>\n      <th>weight</th>\n      <th>red</th>\n      <th>green</th>\n      <th>blue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>10000.000000</td>\n      <td>10000.000000</td>\n      <td>10000.000000</td>\n      <td>10000.000000</td>\n      <td>10000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>9.975685</td>\n      <td>175.050792</td>\n      <td>153.847800</td>\n      <td>76.010600</td>\n      <td>11.363200</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.947844</td>\n      <td>29.212119</td>\n      <td>10.432954</td>\n      <td>11.708433</td>\n      <td>9.061275</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2.960000</td>\n      <td>86.760000</td>\n      <td>115.000000</td>\n      <td>31.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>8.460000</td>\n      <td>152.220000</td>\n      <td>147.000000</td>\n      <td>68.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>9.980000</td>\n      <td>174.985000</td>\n      <td>154.000000</td>\n      <td>76.000000</td>\n      <td>10.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>11.480000</td>\n      <td>197.722500</td>\n      <td>161.000000</td>\n      <td>84.000000</td>\n      <td>17.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>16.450000</td>\n      <td>261.510000</td>\n      <td>192.000000</td>\n      <td>116.000000</td>\n      <td>56.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 201
+    }
+   ],
    "source": [
     "citrus_df.describe()"
    ]
@@ -565,15 +634,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 202,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "c61DljFmzpY9"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "orange           diameter       weight          red        green         blue\ncount  5000.000000  5000.000000  5000.000000  5000.000000  5000.000000\nmean      8.474424   152.804920   156.832800    81.988200     7.115200\nstd       1.260665    18.669021     9.890258    10.090789     6.493779\nmin       2.960000    86.760000   123.000000    49.000000     2.000000\n50%       8.470000   152.665000   157.000000    82.000000     4.000000\nmax      12.870000   231.090000   192.000000   116.000000    38.000000\ngrapefruit           diameter       weight          red        green         blue\ncount  5000.000000  5000.000000  5000.000000  5000.000000  5000.000000\nmean     11.476946   197.296664   150.862800    70.033000    15.611200\nstd       1.221148    19.193190    10.103148    10.044924     9.271592\nmin       7.630000   126.790000   115.000000    31.000000     2.000000\n50%      11.450000   197.430000   151.000000    70.000000    15.000000\nmax      16.450000   261.510000   187.000000   103.000000    56.000000\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your Solution Goes Here"
+    "\n",
+    "orange_df = citrus_df[citrus_df['name'] == 'orange']\n",
+    "grapefruit_df = citrus_df[citrus_df['name'] =='grapefruit']\n",
+    "\n",
+    "print(\"orange\",orange_df.describe(percentiles=[.5]))\n",
+    "\n",
+    "print(\"grapefruit\",grapefruit_df.describe(percentiles=[.5]))"
    ]
   },
   {
@@ -610,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 203,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -637,13 +720,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 204,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "YlBaIjExfPFE"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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10.97, \"weight\": 189.79, \"red\": 145, \"green\": 66, \"blue\": 8}, {\"name\": \"grapefruit\", \"diameter\": 12.16, \"weight\": 207.91, \"red\": 163, \"green\": 73, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 9.01, \"weight\": 160.82, \"red\": 147, \"green\": 79, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.51, \"weight\": 138.27, \"red\": 138, \"green\": 75, \"blue\": 15}, {\"name\": \"orange\", \"diameter\": 5.83, \"weight\": 114.96, \"red\": 173, \"green\": 75, \"blue\": 12}, {\"name\": \"orange\", \"diameter\": 6.52, \"weight\": 123.33, \"red\": 161, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.35, \"weight\": 210.58, \"red\": 159, \"green\": 62, \"blue\": 31}, {\"name\": \"grapefruit\", \"diameter\": 11.18, \"weight\": 192.97, \"red\": 139, \"green\": 62, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.43, \"weight\": 180.78, \"red\": 133, \"green\": 64, \"blue\": 21}, {\"name\": \"grapefruit\", \"diameter\": 12.56, \"weight\": 213.95, \"red\": 151, \"green\": 70, \"blue\": 29}, {\"name\": \"grapefruit\", \"diameter\": 9.88, \"weight\": 172.5, \"red\": 151, \"green\": 69, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 13.73, \"weight\": 232.25, \"red\": 146, \"green\": 60, \"blue\": 15}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 204
+    }
+   ],
    "source": [
     "citrus_df_sample = citrus_df.sample(n=5000, random_state=2020)\n",
     "\n",
@@ -669,13 +764,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 205,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "X9S23tjUAMXo"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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\"red\": 155, \"green\": 61, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.2, \"weight\": 177.54, \"red\": 135, \"green\": 61, \"blue\": 21}, {\"name\": \"orange\", \"diameter\": 9.28, \"weight\": 165.15, \"red\": 169, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.13, \"weight\": 176.2, \"red\": 149, \"green\": 50, \"blue\": 7}, {\"name\": \"grapefruit\", \"diameter\": 10.99, \"weight\": 189.96, \"red\": 152, \"green\": 69, \"blue\": 15}, {\"name\": \"orange\", \"diameter\": 8.43, \"weight\": 152.08, \"red\": 171, \"green\": 100, \"blue\": 6}, {\"name\": \"grapefruit\", \"diameter\": 11.81, \"weight\": 203.04, \"red\": 158, \"green\": 69, \"blue\": 27}, {\"name\": \"orange\", \"diameter\": 8.98, \"weight\": 160.38, \"red\": 131, \"green\": 100, \"blue\": 5}, {\"name\": \"grapefruit\", \"diameter\": 11.78, \"weight\": 202.69, \"red\": 156, \"green\": 69, \"blue\": 16}, {\"name\": \"orange\", \"diameter\": 7.68, \"weight\": 140.58, \"red\": 157, 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\"green\": 62, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.43, \"weight\": 180.78, \"red\": 133, \"green\": 64, \"blue\": 21}, {\"name\": \"grapefruit\", \"diameter\": 12.56, \"weight\": 213.95, \"red\": 151, \"green\": 70, \"blue\": 29}, {\"name\": \"grapefruit\", \"diameter\": 9.88, \"weight\": 172.5, \"red\": 151, \"green\": 69, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 13.73, \"weight\": 232.25, \"red\": 146, \"green\": 60, \"blue\": 15}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": [
+       "alt.HConcatChart(...)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 205
+    }
+   ],
    "source": [
     "alt.Chart(citrus_df_sample, width=400).mark_boxplot().encode(\n",
     "    x='name',\n",
@@ -732,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 206,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -740,7 +847,15 @@
    },
    "outputs": [],
    "source": [
-    "# Your Solution Goes Here"
+    "#new dataframe for diameter and weight\n",
+    "diameter_df = pd.Series(citrus_df['weight'])\n",
+    "weight_df = pd.Series(citrus_df['diameter'])\n",
+    "\n",
+    "#combining to single dataframe\n",
+    "df = pd.merge(diameter_df,weight_df,right_index= True, left_index= True)\n",
+    "\n",
+    "#getting correlation matrix\n",
+    "matrix = df.corr(method ='pearson')"
    ]
   },
   {
@@ -793,13 +908,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 207,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "voAyuNFs7uBy"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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\"green\": 62, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.43, \"weight\": 180.78, \"red\": 133, \"green\": 64, \"blue\": 21}, {\"name\": \"grapefruit\", \"diameter\": 12.56, \"weight\": 213.95, \"red\": 151, \"green\": 70, \"blue\": 29}, {\"name\": \"grapefruit\", \"diameter\": 9.88, \"weight\": 172.5, \"red\": 151, \"green\": 69, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 13.73, \"weight\": 232.25, \"red\": 146, \"green\": 60, \"blue\": 15}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 207
+    }
+   ],
    "source": [
     "alt.Chart(citrus_df_sample).mark_circle().encode(\n",
     "    x='diameter',\n",
@@ -844,13 +971,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 208,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "yCwnzlaqMMPU"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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138.33, \"red\": 157, \"green\": 72, \"blue\": 5}, {\"name\": \"orange\", \"diameter\": 9.09, \"weight\": 162.13, \"red\": 156, \"green\": 67, \"blue\": 5}, {\"name\": \"orange\", \"diameter\": 9.57, \"weight\": 169.65, \"red\": 151, \"green\": 70, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.92, \"weight\": 159.63, \"red\": 164, \"green\": 73, \"blue\": 9}, {\"name\": \"grapefruit\", \"diameter\": 15.59, \"weight\": 256.5, \"red\": 168, \"green\": 82, \"blue\": 20}, {\"name\": \"grapefruit\", \"diameter\": 11.48, \"weight\": 197.91, \"red\": 141, \"green\": 79, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 12.83, \"weight\": 218.29, \"red\": 149, \"green\": 57, \"blue\": 24}, {\"name\": \"grapefruit\", \"diameter\": 10.77, \"weight\": 186.3, \"red\": 163, \"green\": 65, \"blue\": 27}, {\"name\": \"grapefruit\", \"diameter\": 13.86, \"weight\": 234.8, \"red\": 156, \"green\": 66, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.81, \"weight\": 187.03, \"red\": 134, \"green\": 75, \"blue\": 25}, {\"name\": \"orange\", \"diameter\": 8.22, \"weight\": 148.81, \"red\": 158, \"green\": 86, \"blue\": 6}, {\"name\": \"grapefruit\", \"diameter\": 12.97, \"weight\": 220.38, \"red\": 148, \"green\": 59, \"blue\": 13}, {\"name\": \"orange\", \"diameter\": 8.13, \"weight\": 147.59, \"red\": 169, \"green\": 92, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.89, \"weight\": 159.04, \"red\": 156, \"green\": 84, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.04, \"weight\": 175.07, \"red\": 169, \"green\": 76, \"blue\": 18}, {\"name\": \"grapefruit\", \"diameter\": 10.7, \"weight\": 184.96, \"red\": 165, \"green\": 71, \"blue\": 7}, {\"name\": \"orange\", \"diameter\": 8.01, \"weight\": 145.8, \"red\": 165, \"green\": 68, \"blue\": 11}, {\"name\": \"orange\", \"diameter\": 8.35, \"weight\": 150.54, \"red\": 157, \"green\": 70, \"blue\": 13}, {\"name\": \"orange\", \"diameter\": 7.1, \"weight\": 132.98, \"red\": 161, \"green\": 64, \"blue\": 20}, {\"name\": \"orange\", \"diameter\": 7.77, \"weight\": 141.76, \"red\": 149, \"green\": 78, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 9.0, \"weight\": 160.7, \"red\": 150, \"green\": 87, \"blue\": 16}, {\"name\": \"grapefruit\", \"diameter\": 9.52, \"weight\": 166.17, \"red\": 155, \"green\": 53, \"blue\": 8}, {\"name\": \"grapefruit\", \"diameter\": 13.29, \"weight\": 225.2, \"red\": 149, \"green\": 73, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.34, \"weight\": 135.9, \"red\": 168, \"green\": 86, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.03, \"weight\": 206.06, \"red\": 135, \"green\": 53, \"blue\": 28}, {\"name\": \"orange\", \"diameter\": 10.83, \"weight\": 186.64, \"red\": 147, \"green\": 66, \"blue\": 3}, {\"name\": \"orange\", \"diameter\": 7.65, \"weight\": 140.11, \"red\": 161, \"green\": 92, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.96, \"weight\": 144.89, \"red\": 158, \"green\": 83, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.45, \"weight\": 181.12, \"red\": 154, \"green\": 73, \"blue\": 11}, {\"name\": \"orange\", \"diameter\": 9.97, \"weight\": 175.75, \"red\": 171, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.48, \"weight\": 212.72, \"red\": 134, \"green\": 78, \"blue\": 18}, {\"name\": \"orange\", \"diameter\": 8.92, \"weight\": 159.53, \"red\": 144, \"green\": 72, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.17, \"weight\": 148.15, \"red\": 149, \"green\": 74, \"blue\": 17}, {\"name\": \"orange\", \"diameter\": 6.6, \"weight\": 124.91, \"red\": 157, \"green\": 97, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.19, \"weight\": 177.39, \"red\": 154, \"green\": 72, \"blue\": 11}, {\"name\": \"grapefruit\", \"diameter\": 13.35, \"weight\": 226.71, \"red\": 168, \"green\": 76, \"blue\": 17}, {\"name\": \"grapefruit\", \"diameter\": 11.87, \"weight\": 203.78, \"red\": 143, \"green\": 78, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.58, \"weight\": 153.86, \"red\": 176, \"green\": 90, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.04, \"weight\": 190.74, \"red\": 140, \"green\": 79, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 10.18, \"weight\": 176.96, \"red\": 159, \"green\": 74, \"blue\": 31}, {\"name\": \"grapefruit\", \"diameter\": 8.24, \"weight\": 146.47, \"red\": 149, \"green\": 83, \"blue\": 19}, {\"name\": \"orange\", \"diameter\": 8.54, \"weight\": 153.34, \"red\": 182, \"green\": 89, \"blue\": 16}, {\"name\": \"orange\", \"diameter\": 8.58, \"weight\": 153.91, \"red\": 143, \"green\": 68, \"blue\": 14}, {\"name\": \"grapefruit\", \"diameter\": 8.97, \"weight\": 155.84, \"red\": 146, \"green\": 75, \"blue\": 7}, {\"name\": \"orange\", \"diameter\": 9.49, \"weight\": 168.43, \"red\": 156, \"green\": 66, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.14, \"weight\": 207.5, \"red\": 137, \"green\": 85, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 12.78, \"weight\": 217.62, \"red\": 148, \"green\": 81, \"blue\": 20}, {\"name\": \"grapefruit\", \"diameter\": 11.48, \"weight\": 197.96, \"red\": 151, \"green\": 72, \"blue\": 19}, {\"name\": \"orange\", \"diameter\": 6.88, \"weight\": 129.37, \"red\": 152, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.4, \"weight\": 211.29, \"red\": 153, \"green\": 69, \"blue\": 18}, {\"name\": \"grapefruit\", \"diameter\": 11.78, \"weight\": 202.62, \"red\": 147, \"green\": 81, \"blue\": 24}, {\"name\": \"grapefruit\", \"diameter\": 13.62, \"weight\": 230.89, \"red\": 159, \"green\": 81, \"blue\": 24}, {\"name\": \"orange\", \"diameter\": 6.5, \"weight\": 123.18, \"red\": 159, \"green\": 108, \"blue\": 12}, {\"name\": \"orange\", \"diameter\": 6.94, \"weight\": 130.73, \"red\": 172, \"green\": 89, \"blue\": 3}, {\"name\": \"grapefruit\", \"diameter\": 11.87, \"weight\": 203.84, \"red\": 159, \"green\": 62, \"blue\": 15}, {\"name\": \"orange\", \"diameter\": 10.73, \"weight\": 185.03, \"red\": 141, \"green\": 84, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.28, \"weight\": 178.41, \"red\": 162, \"green\": 66, \"blue\": 24}, {\"name\": \"grapefruit\", \"diameter\": 12.67, \"weight\": 215.76, \"red\": 160, \"green\": 69, \"blue\": 16}, {\"name\": \"orange\", \"diameter\": 7.59, \"weight\": 139.34, \"red\": 164, \"green\": 94, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 14.51, \"weight\": 242.96, \"red\": 147, \"green\": 85, \"blue\": 19}, {\"name\": \"orange\", \"diameter\": 8.01, \"weight\": 145.71, \"red\": 170, \"green\": 64, \"blue\": 6}, {\"name\": \"orange\", \"diameter\": 7.09, \"weight\": 132.73, \"red\": 161, \"green\": 99, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.0, \"weight\": 190.11, \"red\": 147, \"green\": 81, \"blue\": 16}, {\"name\": \"grapefruit\", \"diameter\": 10.95, \"weight\": 189.34, \"red\": 150, \"green\": 82, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.45, \"weight\": 181.06, \"red\": 151, \"green\": 71, \"blue\": 34}, {\"name\": \"orange\", \"diameter\": 12.04, \"weight\": 207.91, \"red\": 164, \"green\": 74, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.35, \"weight\": 136.01, \"red\": 149, \"green\": 73, \"blue\": 16}, {\"name\": \"grapefruit\", \"diameter\": 11.09, \"weight\": 191.71, \"red\": 161, \"green\": 86, \"blue\": 5}, {\"name\": \"grapefruit\", \"diameter\": 10.35, \"weight\": 179.36, \"red\": 153, \"green\": 73, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 11.9, \"weight\": 204.21, \"red\": 152, \"green\": 65, \"blue\": 11}, {\"name\": \"orange\", \"diameter\": 10.49, \"weight\": 182.32, \"red\": 152, \"green\": 73, \"blue\": 9}, {\"name\": \"grapefruit\", \"diameter\": 12.64, \"weight\": 215.5, \"red\": 140, \"green\": 83, \"blue\": 17}, {\"name\": \"orange\", \"diameter\": 9.42, \"weight\": 167.25, \"red\": 174, \"green\": 80, \"blue\": 12}, {\"name\": \"orange\", \"diameter\": 9.33, \"weight\": 165.76, \"red\": 160, \"green\": 82, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.15, \"weight\": 192.63, \"red\": 153, \"green\": 72, \"blue\": 19}, {\"name\": \"grapefruit\", \"diameter\": 12.49, \"weight\": 212.98, \"red\": 150, \"green\": 74, \"blue\": 16}, {\"name\": \"orange\", \"diameter\": 8.17, \"weight\": 148.18, \"red\": 155, \"green\": 62, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.85, \"weight\": 203.51, \"red\": 138, \"green\": 71, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.64, \"weight\": 154.79, \"red\": 163, \"green\": 74, \"blue\": 22}, {\"name\": \"orange\", \"diameter\": 8.16, \"weight\": 148.03, \"red\": 161, \"green\": 86, \"blue\": 23}, {\"name\": \"grapefruit\", \"diameter\": 13.19, \"weight\": 223.76, \"red\": 147, \"green\": 83, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 12.3, \"weight\": 209.94, \"red\": 140, \"green\": 68, \"blue\": 3}, {\"name\": \"grapefruit\", \"diameter\": 11.05, \"weight\": 190.84, \"red\": 141, \"green\": 54, \"blue\": 4}, {\"name\": \"orange\", \"diameter\": 6.57, \"weight\": 124.04, \"red\": 144, \"green\": 86, \"blue\": 7}, {\"name\": \"orange\", \"diameter\": 6.9, \"weight\": 129.8, \"red\": 149, \"green\": 83, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 13.19, \"weight\": 223.72, \"red\": 155, \"green\": 60, \"blue\": 27}, {\"name\": \"grapefruit\", \"diameter\": 10.44, \"weight\": 180.9, \"red\": 144, \"green\": 81, \"blue\": 35}, {\"name\": \"grapefruit\", \"diameter\": 11.17, \"weight\": 192.84, \"red\": 155, \"green\": 78, \"blue\": 33}, {\"name\": \"orange\", \"diameter\": 10.56, \"weight\": 183.13, \"red\": 169, \"green\": 90, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.62, \"weight\": 199.89, \"red\": 133, \"green\": 66, \"blue\": 28}, {\"name\": \"orange\", \"diameter\": 9.77, \"weight\": 172.88, \"red\": 149, \"green\": 82, \"blue\": 9}, {\"name\": \"orange\", \"diameter\": 6.75, \"weight\": 127.01, \"red\": 169, \"green\": 89, \"blue\": 14}, {\"name\": \"orange\", \"diameter\": 8.73, \"weight\": 156.45, \"red\": 165, \"green\": 72, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.36, \"weight\": 136.01, \"red\": 171, \"green\": 87, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.08, \"weight\": 191.57, \"red\": 147, \"green\": 96, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 12.75, \"weight\": 217.1, \"red\": 170, \"green\": 77, \"blue\": 7}, {\"name\": \"orange\", \"diameter\": 8.77, \"weight\": 156.92, \"red\": 164, \"green\": 84, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 11.09, \"weight\": 191.63, \"red\": 156, \"green\": 72, \"blue\": 21}, {\"name\": \"grapefruit\", \"diameter\": 11.41, \"weight\": 196.83, \"red\": 166, \"green\": 75, \"blue\": 13}, {\"name\": \"orange\", \"diameter\": 7.19, \"weight\": 133.9, \"red\": 164, \"green\": 67, \"blue\": 13}, {\"name\": \"orange\", \"diameter\": 5.51, \"weight\": 109.59, \"red\": 157, \"green\": 82, \"blue\": 3}, {\"name\": \"orange\", \"diameter\": 7.53, \"weight\": 138.6, \"red\": 153, \"green\": 87, \"blue\": 7}, {\"name\": \"grapefruit\", \"diameter\": 10.27, \"weight\": 178.39, \"red\": 149, \"green\": 68, \"blue\": 14}, {\"name\": \"orange\", \"diameter\": 8.09, \"weight\": 146.95, \"red\": 146, \"green\": 89, \"blue\": 13}, {\"name\": \"orange\", \"diameter\": 9.93, \"weight\": 175.11, \"red\": 161, \"green\": 93, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.01, \"weight\": 131.73, \"red\": 153, \"green\": 75, \"blue\": 10}, {\"name\": \"orange\", \"diameter\": 9.96, \"weight\": 175.65, \"red\": 162, \"green\": 73, \"blue\": 14}, {\"name\": \"orange\", \"diameter\": 8.77, \"weight\": 157.09, \"red\": 160, \"green\": 67, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.35, \"weight\": 210.64, \"red\": 143, \"green\": 72, \"blue\": 19}, {\"name\": \"grapefruit\", \"diameter\": 11.55, \"weight\": 198.98, \"red\": 167, \"green\": 65, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.04, \"weight\": 206.28, \"red\": 147, \"green\": 74, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.3, \"weight\": 195.05, \"red\": 155, \"green\": 61, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.2, \"weight\": 177.54, \"red\": 135, \"green\": 61, \"blue\": 21}, {\"name\": \"orange\", \"diameter\": 9.28, \"weight\": 165.15, \"red\": 169, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.13, \"weight\": 176.2, \"red\": 149, \"green\": 50, \"blue\": 7}, {\"name\": \"grapefruit\", \"diameter\": 10.99, \"weight\": 189.96, \"red\": 152, \"green\": 69, \"blue\": 15}, {\"name\": \"orange\", \"diameter\": 8.43, \"weight\": 152.08, \"red\": 171, \"green\": 100, \"blue\": 6}, {\"name\": \"grapefruit\", \"diameter\": 11.81, \"weight\": 203.04, \"red\": 158, \"green\": 69, \"blue\": 27}, {\"name\": \"orange\", \"diameter\": 8.98, \"weight\": 160.38, \"red\": 131, \"green\": 100, \"blue\": 5}, {\"name\": \"grapefruit\", \"diameter\": 11.78, \"weight\": 202.69, \"red\": 156, \"green\": 69, \"blue\": 16}, {\"name\": \"orange\", \"diameter\": 7.68, \"weight\": 140.58, \"red\": 157, \"green\": 90, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 11.45, \"weight\": 197.46, \"red\": 149, \"green\": 53, \"blue\": 21}, {\"name\": \"orange\", \"diameter\": 6.78, \"weight\": 127.41, \"red\": 162, \"green\": 70, \"blue\": 23}, {\"name\": \"orange\", \"diameter\": 8.44, \"weight\": 152.16, \"red\": 171, \"green\": 67, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 9.24, \"weight\": 164.42, \"red\": 177, \"green\": 86, \"blue\": 9}, {\"name\": \"grapefruit\", \"diameter\": 12.19, \"weight\": 208.23, \"red\": 152, \"green\": 79, \"blue\": 16}, {\"name\": \"grapefruit\", \"diameter\": 9.38, \"weight\": 163.83, \"red\": 143, \"green\": 66, \"blue\": 18}, {\"name\": \"orange\", \"diameter\": 9.1, \"weight\": 162.28, \"red\": 141, \"green\": 89, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.68, \"weight\": 215.85, \"red\": 154, \"green\": 79, \"blue\": 17}, {\"name\": \"grapefruit\", \"diameter\": 11.01, \"weight\": 190.44, \"red\": 142, \"green\": 82, \"blue\": 12}, {\"name\": \"orange\", \"diameter\": 8.2, \"weight\": 148.52, \"red\": 156, \"green\": 98, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.39, \"weight\": 180.07, \"red\": 149, \"green\": 64, \"blue\": 18}, {\"name\": \"orange\", \"diameter\": 6.38, \"weight\": 121.55, \"red\": 156, \"green\": 80, \"blue\": 17}, {\"name\": \"orange\", \"diameter\": 8.03, \"weight\": 145.96, \"red\": 162, \"green\": 85, \"blue\": 11}, {\"name\": \"orange\", \"diameter\": 9.89, \"weight\": 174.58, \"red\": 144, \"green\": 89, \"blue\": 22}, {\"name\": \"grapefruit\", \"diameter\": 11.23, \"weight\": 194.02, \"red\": 139, \"green\": 89, \"blue\": 25}, {\"name\": \"orange\", \"diameter\": 7.84, \"weight\": 143.12, \"red\": 158, \"green\": 87, \"blue\": 5}, {\"name\": \"orange\", \"diameter\": 9.33, \"weight\": 165.84, \"red\": 176, \"green\": 96, \"blue\": 20}, {\"name\": \"orange\", \"diameter\": 8.97, \"weight\": 160.35, \"red\": 152, \"green\": 86, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.94, \"weight\": 189.22, \"red\": 141, \"green\": 74, \"blue\": 14}, {\"name\": \"orange\", \"diameter\": 9.76, \"weight\": 172.5, \"red\": 157, \"green\": 68, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.17, \"weight\": 192.75, \"red\": 172, \"green\": 53, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.13, \"weight\": 192.39, \"red\": 164, \"green\": 66, \"blue\": 8}, {\"name\": \"grapefruit\", \"diameter\": 11.08, \"weight\": 191.47, \"red\": 146, \"green\": 60, \"blue\": 10}, {\"name\": \"orange\", \"diameter\": 9.21, \"weight\": 163.83, \"red\": 144, \"green\": 89, \"blue\": 4}, {\"name\": \"grapefruit\", \"diameter\": 10.21, \"weight\": 177.66, \"red\": 140, \"green\": 55, \"blue\": 14}, {\"name\": \"orange\", \"diameter\": 6.67, \"weight\": 125.88, \"red\": 155, \"green\": 93, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 9.15, \"weight\": 159.98, \"red\": 153, \"green\": 59, \"blue\": 23}, {\"name\": \"orange\", \"diameter\": 7.81, \"weight\": 142.54, \"red\": 137, \"green\": 92, \"blue\": 19}, {\"name\": \"grapefruit\", \"diameter\": 12.53, \"weight\": 213.52, \"red\": 153, \"green\": 48, \"blue\": 5}, {\"name\": \"orange\", \"diameter\": 9.0, \"weight\": 160.6, \"red\": 154, \"green\": 96, \"blue\": 17}, {\"name\": \"orange\", \"diameter\": 9.98, \"weight\": 175.78, \"red\": 156, \"green\": 103, \"blue\": 20}, {\"name\": \"orange\", \"diameter\": 7.69, \"weight\": 140.71, \"red\": 153, \"green\": 81, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 10.58, \"weight\": 183.33, \"red\": 172, \"green\": 79, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.31, \"weight\": 149.87, \"red\": 165, \"green\": 83, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 10.0, \"weight\": 174.45, \"red\": 147, \"green\": 78, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 10.16, \"weight\": 178.01, \"red\": 166, \"green\": 83, \"blue\": 26}, {\"name\": \"grapefruit\", \"diameter\": 12.03, \"weight\": 206.06, \"red\": 151, \"green\": 66, \"blue\": 4}, {\"name\": \"orange\", \"diameter\": 9.01, \"weight\": 160.85, \"red\": 156, \"green\": 68, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 9.85, \"weight\": 172.21, \"red\": 139, \"green\": 75, \"blue\": 30}, {\"name\": \"grapefruit\", \"diameter\": 12.85, \"weight\": 218.59, \"red\": 149, \"green\": 80, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 10.69, \"weight\": 184.73, \"red\": 144, \"green\": 66, \"blue\": 25}, {\"name\": \"orange\", \"diameter\": 7.35, \"weight\": 136.0, \"red\": 149, \"green\": 83, \"blue\": 19}, {\"name\": \"orange\", \"diameter\": 7.19, \"weight\": 133.9, \"red\": 157, \"green\": 79, \"blue\": 8}, {\"name\": \"grapefruit\", \"diameter\": 11.75, \"weight\": 202.24, \"red\": 155, \"green\": 64, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.48, \"weight\": 137.95, \"red\": 152, \"green\": 71, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 8.07, \"weight\": 146.59, \"red\": 153, \"green\": 92, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 11.69, \"weight\": 201.12, \"red\": 138, \"green\": 81, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.97, \"weight\": 189.79, \"red\": 145, \"green\": 66, \"blue\": 8}, {\"name\": \"grapefruit\", \"diameter\": 12.16, \"weight\": 207.91, \"red\": 163, \"green\": 73, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 9.01, \"weight\": 160.82, \"red\": 147, \"green\": 79, \"blue\": 2}, {\"name\": \"orange\", \"diameter\": 7.51, \"weight\": 138.27, \"red\": 138, \"green\": 75, \"blue\": 15}, {\"name\": \"orange\", \"diameter\": 5.83, \"weight\": 114.96, \"red\": 173, \"green\": 75, \"blue\": 12}, {\"name\": \"orange\", \"diameter\": 6.52, \"weight\": 123.33, \"red\": 161, \"green\": 77, \"blue\": 2}, {\"name\": \"grapefruit\", \"diameter\": 12.35, \"weight\": 210.58, \"red\": 159, \"green\": 62, \"blue\": 31}, {\"name\": \"grapefruit\", \"diameter\": 11.18, \"weight\": 192.97, \"red\": 139, \"green\": 62, \"blue\": 10}, {\"name\": \"grapefruit\", \"diameter\": 10.43, \"weight\": 180.78, \"red\": 133, \"green\": 64, \"blue\": 21}, {\"name\": \"grapefruit\", \"diameter\": 12.56, \"weight\": 213.95, \"red\": 151, \"green\": 70, \"blue\": 29}, {\"name\": \"grapefruit\", \"diameter\": 9.88, \"weight\": 172.5, \"red\": 151, \"green\": 69, \"blue\": 25}, {\"name\": \"grapefruit\", \"diameter\": 13.73, \"weight\": 232.25, \"red\": 146, \"green\": 60, \"blue\": 15}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": [
+       "alt.HConcatChart(...)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 208
+    }
+   ],
    "source": [
     "alt.Chart(citrus_df_sample, width=400).mark_boxplot().encode(\n",
     "    x='name',\n",
@@ -898,13 +1037,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 209,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "PfZyWY4_Lqo7"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "name\n",
+       "grapefruit    5000\n",
+       "orange        5000\n",
+       "Name: name, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 209
+    }
+   ],
    "source": [
     "citrus_df.groupby('name')['name'].count()"
    ]
@@ -925,13 +1078,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 210,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "SR96sFvDLnCr"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fb0d0bc6760>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 210
+    },
+    {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "from PIL import Image\n",
     "from matplotlib.pyplot import imshow\n",
@@ -986,15 +1161,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 211,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "pWmg9cLXQlWA"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fb0f3485070>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 211
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
-    "# Your Solution Goes Here"
+    "height, width = 100, 100\n",
+    "\n",
+    "\n",
+    "\n",
+    "img = Image.new('RGB', (width, height), color=(255, 255, 255))\n",
+    "pixels = img.load()\n",
+    "row_i, col_i = 0, 0\n",
+    "for _, fruit in citrus_df.iterrows():\n",
+    "  pixels[col_i, row_i] = (fruit['red'], fruit['green'], fruit['blue'])\n",
+    "  col_i += 1\n",
+    "  if col_i >= width:\n",
+    "    col_i = 0\n",
+    "    row_i += 1\n",
+    "\n",
+    "\n",
+    "\n",
+    "imshow(img)"
    ]
   },
   {
@@ -1058,13 +1271,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 212,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "AV0d1qhhOxml"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Index(['name', 'diameter', 'weight', 'red', 'green', 'blue'], dtype='object')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 212
+    }
+   ],
    "source": [
     "citrus_df.columns"
    ]
@@ -1103,15 +1327,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 213,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "SlRxZuRSXlWx"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "            name  diameter  weight  red  green  blue\n0         orange      2.96   86.76  172     85     2\n1         orange      3.91   88.05  166     78     3\n2         orange      4.42   95.17  156     81     2\n3         orange      4.47   95.60  163     81     4\n4         orange      4.48   95.76  161     72     9\n...          ...       ...     ...  ...    ...   ...\n9995  grapefruit     15.35  253.89  149     77    20\n9996  grapefruit     15.41  254.67  148     68     7\n9997  grapefruit     15.59  256.50  168     82    20\n9998  grapefruit     15.92  260.14  142     72    11\n9999  grapefruit     16.45  261.51  152     74     2\n\n[10000 rows x 6 columns]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your Solution Goes Here"
+    "new_df = citrus_df['name'] == 'orange'\n",
+    "print(citrus_df)\n",
+    "#citrus_df.columns = ('name','diameter','weight','red','green','blue')\n",
+    "#citrus_df.append(new_df)\n",
+    "#citrus_df.insert(loc=2, column =\"is_orange\",value =new_df)\n",
+    "#itrus.df\n",
+    "#citrus_df.append(new_df)"
    ]
   },
   {
@@ -1148,13 +1386,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 214,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "6dtKjRtAYtsg"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "KeyError",
+     "evalue": "'is_orange'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-214-59e98aa0e9e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcitrus_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'is_orange'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'is_orange'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   6509\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6511\u001b[0;31m         return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m   6512\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6513\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m    526\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    779\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    782\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'is_orange'"
+     ]
+    }
+   ],
    "source": [
     "citrus_df.groupby('is_orange')['is_orange'].count()"
    ]
@@ -1173,13 +1426,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 146,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "XDdyNHZgZEUN"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['orange'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 146
+    }
+   ],
    "source": [
     "citrus_df[citrus_df['is_orange']]['name'].unique()"
    ]
@@ -1216,13 +1480,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 148,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "YuE6tX8qO3fF"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "('is_orange', ['diameter', 'weight', 'red', 'green', 'blue'])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 148
+    }
+   ],
    "source": [
     "target_column = 'is_orange'\n",
     "\n",
@@ -1245,13 +1520,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 149,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "E9y3IXRaeYPD"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "is_orange\n",
+       "False    4030\n",
+       "True     3970\n",
+       "Name: is_orange, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 149
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -1277,13 +1566,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 150,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "duQC2BDgnDJt"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "is_orange\n",
+       "False     970\n",
+       "True     1030\n",
+       "Name: is_orange, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 150
+    }
+   ],
    "source": [
     "y_test.groupby(y_test).count()"
    ]
@@ -1322,15 +1625,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 158,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "cJ1Cf4YMwutU"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "is_orange\n",
+       "False    4000\n",
+       "True     4000\n",
+       "Name: is_orange, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 158
+    }
+   ],
    "source": [
-    "# Your Code Goes Here"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    citrus_df[feature_columns],\n",
+    "    citrus_df[target_column],\n",
+    "    test_size=0.2,\n",
+    "    random_state=180,\n",
+    "    shuffle=True,\n",
+    "    stratify = citrus_df[target_column])\n",
+    "\n",
+    "y_train.groupby(y_train).count()"
    ]
   },
   {
@@ -1365,13 +1692,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 159,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "M05Xb00YQce_"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "((8000, 5), (8000,))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 159
+    }
+   ],
    "source": [
     "X_train.shape, y_train.shape"
    ]
@@ -1411,13 +1749,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 160,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "CYqtUfInQw2K"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "count     8000\n",
+       "unique       2\n",
+       "top       True\n",
+       "freq      4000\n",
+       "Name: is_orange, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 160
+    }
+   ],
    "source": [
     "y_train.describe()"
    ]
@@ -1436,13 +1789,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 161,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "JgTAtdGRRdzZ"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "count     2000\n",
+       "unique       2\n",
+       "top       True\n",
+       "freq      1000\n",
+       "Name: is_orange, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 161
+    }
+   ],
    "source": [
     "y_test.describe()"
    ]
@@ -1459,13 +1827,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 162,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "pwnrwBbKTLo6"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "is_orange\n",
+       "False    1000\n",
+       "True     1000\n",
+       "Name: is_orange, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 162
+    }
+   ],
    "source": [
     "y_test.groupby(by=y_test).count()"
    ]
@@ -1494,13 +1876,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 215,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "jCmKNLvDTgti"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "LogisticRegression(random_state=2020)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 215
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
@@ -1555,13 +1948,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 231,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "aIESA8B8WYgn"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy:  0.497\nPrecision:  0.48\nRecall:  0.497\nF1:  0.488\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
     "\n",
@@ -1639,7 +2040,7 @@
     "id": "9yEN7fQNvqK1"
    },
    "source": [
-    "*Please Put Your Answer Here*"
+    ">False positive would mean the model identified an orange when it was actually grapefruit and a false negative would be the opposite"
    ]
   },
   {
@@ -1674,13 +2075,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 216,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "X4vXHTwvY4aV"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 385.78125 262.19625\" width=\"385.78125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-06-28T14:25:25.850494</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "from sklearn.metrics import roc_curve\n",
     "from sklearn.metrics import roc_auc_score\n",
@@ -1727,13 +2140,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 217,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "YyvmOGaHluCl"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "from sklearn.metrics import precision_recall_curve\n",
     "\n",
@@ -1819,13 +2244,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 227,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "VrFSNQv1onp4"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "LogisticRegression(C=1, random_state=2020, solver='newton-cg', tol=0.1)\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1851,12 +2284,21 @@
     "    random_state=2020,\n",
     ")\n",
     "\n",
-    "search = GridSearchCV(model, {\n",
-    "  # Your Solution Goes Here\n",
-    "})\n",
+    "param_grid = {'tol': [0.1, 1, 10, 100],  \n",
+    "              'C': [1, 0.1, 0.01, 0.001, 0.0001], \n",
+    "              'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
+    "              'max_iter': [100]}\n",
+    "\n",
+    "\n",
+    "search = GridSearchCV(model,param_grid)\n",
+    "\n",
     "\n",
     "search.fit(X_train, y_train)\n",
     "\n",
+    "model = search.best_estimator_\n",
+    "\n",
+    "model.fit(X_train,y_train)\n",
+    "predictions = model.predict(X_validate)\n",
     "print(search.best_estimator_)"
    ]
   },
@@ -1896,15 +2338,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "      diameter  weight  red  green  blue\n7919     11.72  201.62  138     73    17\n911       7.34  135.89  166     69     2\n6631     10.92  188.84  152     77    18\n4536     10.13  177.58  138     97     7\n8486     12.11  207.28  157     74     9\n...        ...     ...  ...    ...   ...\n598       6.99  131.25  152    100    12\n8354     12.03  206.00  145     63     5\n4744     10.55  183.04  150     71     7\n4566     10.18  178.43  159     73    19\n2313      8.37  150.94  154     76     6\n\n[2000 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_validate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "4RpgwipWsmB8"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy:  0.957\nPrecision:  0.94\nRecall:  0.973\nF1:  0.956\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your Solution Goes Here"
+    "print('Accuracy: ', round(accuracy_score(predictions, y_validate), 3))\n",
+    "print('Precision: ', round(precision_score(predictions, y_validate), 3))\n",
+    "print('Recall: ', round(recall_score(predictions, y_validate), 3))\n",
+    "print('F1: ', round(f1_score(predictions, y_validate), 3))"
    ]
   },
   {
@@ -1930,24 +2400,30 @@
    ]
   },
   {
+   "source": [
+    "Before\n",
+    "\n",
+    "\n",
+    "Accuracy:  0.497.\n",
+    "\n",
+    "Precision:  0.48. \n",
+    "\n",
+    "Recall:  0.497. \n",
+    "\n",
+    "F1:  0.488. "
+   ],
    "cell_type": "markdown",
    "metadata": {
     "colab_type": "text",
     "id": "YL74G6Jk4d9C"
-   },
-   "source": [
-    "##### **Student Solution**"
-   ]
+   }
   },
   {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "SNzRzEB8tHaZ"
-   },
    "source": [
-    "*Please Put Your Answer Here*"
-   ]
+    ">All validation parameters improved with grid search. Everything was numerically close to 1."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
@@ -1988,7 +2464,7 @@
     "id": "vIR8qHyyuVPy"
    },
    "source": [
-    "*Please Put Your Answer Here*"
+    ">The machine learning algorithm developed was not a good fit for the original problem. The model only took into account whether or not a specfic citrus fruit was an orange or not. The original dataset only took into account the oranges and the grapefruit."
    ]
   },
   {
@@ -2042,7 +2518,7 @@
     "id": "tibOqWcM1UIP"
    },
    "source": [
-    "*Please Put Your Answer Here*"
+    ">It depends on the dataset entirely. Differnt methods work for the kinds of varibales and features that are being measured and tested."
    ]
   },
   {
@@ -2068,28 +2544,106 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Standard Scalar\n",
+    "citrus_df[feature_columns] = StandardScaler().fit_transform(citrus_df[feature_columns])\n",
+    "\n",
+    "\n",
+    "\n",
+    "X_train, X_validate, y_train, y_validate = train_test_split(\n",
+    "    citrus_df[feature_columns],\n",
+    "    citrus_df[target_column],\n",
+    "    test_size=0.2,\n",
+    "    random_state=42,\n",
+    "    shuffle=True,\n",
+    "    stratify=citrus_df[target_column])\n",
+    "\n",
+    "model = LogisticRegression(\n",
+    "    random_state=2020,\n",
+    ")\n",
+    "\n",
+    "param_grid = {'tol': [0.1, 1, 10, 100],  \n",
+    "              'C': [1, 0.1, 0.01, 0.001, 0.0001], \n",
+    "              'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
+    "              'max_iter': [100]}\n",
+    "\n",
+    "\n",
+    "search = GridSearchCV(model,param_grid)\n",
+    "\n",
+    "\n",
+    "search.fit(X_train, y_train)\n",
+    "\n",
+    "model = search.best_estimator_\n",
+    "\n",
+    "model.fit(X_train,y_train)\n",
+    "predictions = model.predict(X_validate)\n",
+    "print(search.best_estimator_)\n",
+    "\n",
+    "\n",
+    "#scalar.fit(citrus_df)\n",
+    "#scalar\n"
+   ],
+   "cell_type": "code",
    "metadata": {
     "colab_type": "text",
     "id": "Nmv-j6oR4lFK"
    },
-   "source": [
-    "#### **Student Solution**"
+   "execution_count": 244,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "LogisticRegression(C=1, random_state=2020, solver='newton-cg', tol=0.1)\n"
+     ]
+    }
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": 246,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "YLTLFHgZyP_r"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy:  0.942\nPrecision:  0.935\nRecall:  0.948\nF1:  0.942\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "print('Accuracy: ', round(accuracy_score(predictions, y_validate), 3))\n",
+    "print('Precision: ', round(precision_score(predictions, y_validate), 3))\n",
+    "print('Recall: ', round(recall_score(predictions, y_validate), 3))\n",
+    "print('F1: ', round(f1_score(predictions, y_validate), 3))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2129,7 +2683,7 @@
     "id": "XBy3g7pz4win"
    },
    "source": [
-    "*Please Put Your Answer Here*"
+    "> Though the the optimal hyperparameters were the same, The representative Accuracy, Precision, Recall, and F1 parameters all decreased. It all has to do with the magnitude and spread of the data. Using the standard scalar involves looking at the spread of the data, where the magnitude is not important. The lower validation meaasures means that the magnitude was important and that the present raw data turned out to be important in developing the algorithm."
    ]
   },
   {
@@ -2170,10 +2724,13 @@
    "toc_visible": true
   },
   "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "20fdcea9dd39c58b400bbbafc3c219f020dc49d749b7a36aee9879bd1518e1db"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file
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