Assg-Community/LR - Placement Data Assg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6e56e07",
   "metadata": {},
   "source": [
    "# step 1: Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "910b1273",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c73051",
   "metadata": {},
   "source": [
    "# step 2: Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84c38db7",
   "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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.54</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cgpa  placement_exam_marks  placed\n",
       "0  7.19                  26.0       1\n",
       "1  7.46                  38.0       1\n",
       "2  7.54                  40.0       1\n",
       "3  6.42                   8.0       1\n",
       "4  7.23                  17.0       0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'https://raw.githubusercontent.com/edyoda/DS31032023/main/Machine%20Learning/August/26-8-2023/Assignment/placement.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7118a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bd8bbc",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "- cgpa and placement_exam_marks are the two independent variable in the above data.\n",
    "- The placed column is dependent on the cgpa and placement_exam_marks column. Hence placed is the dependent or target variable in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b2fefe",
   "metadata": {},
   "source": [
    "# step 3: Data cleaning, data wrangling, data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06561c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cgpa                    0\n",
       "placement_exam_marks    0\n",
       "placed                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0accebe",
   "metadata": {},
   "source": [
    "- No missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b28aeb",
   "metadata": {},
   "source": [
    "### Check the relation between dependent and independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "253b5721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3c345eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "220560ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "mms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "018695a1",
   "metadata": {
    "scrolled": true
   },
   "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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "      <th>placed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.543735</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.607565</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.626478</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.361702</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cgpa  placement_exam_marks  placed\n",
       "0  0.543735                  0.26       1\n",
       "1  0.607565                  0.38       1\n",
       "2  0.626478                  0.40       1\n",
       "3  0.361702                  0.08       1\n",
       "4  0.553191                  0.17       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['cgpa','placement_exam_marks']] = mms.fit_transform(data[['cgpa','placement_exam_marks']])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95878796",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 4: separate the data into x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a31ad4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('placed',axis=1)\n",
    "y = data['placed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc6bcba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 5: split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39f01ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421c86d5",
   "metadata": {},
   "source": [
    "- The target variable is an object data. Hence , we have to apply classification ML algorithm in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "009bf212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 6: Linear regression applied on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "760f8f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we apply linear regression in the above data...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6793a214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "71433a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b28dad1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 7: Perform predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92ec3ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49660957, 0.4914847 , 0.46158968, 0.51487992, 0.52423364,\n",
       "       0.49840662, 0.47082617, 0.47775309, 0.50098246, 0.51126401,\n",
       "       0.50044994, 0.51383986, 0.48740488, 0.50055035, 0.49001935,\n",
       "       0.52999328, 0.50212976, 0.50928613, 0.50139958, 0.49802313,\n",
       "       0.49546411, 0.49920723, 0.44769222, 0.44410495, 0.53497911,\n",
       "       0.49449764, 0.5100213 , 0.49334036, 0.48124677, 0.49662639,\n",
       "       0.49800132, 0.52646779, 0.50597512, 0.48920875, 0.51568552,\n",
       "       0.49663638, 0.48693597, 0.4793693 , 0.51433558, 0.4435706 ,\n",
       "       0.51059745, 0.50679754, 0.50536034, 0.51189877, 0.47027684,\n",
       "       0.4838694 , 0.51022395, 0.50853914, 0.48872302, 0.51991937,\n",
       "       0.48337868, 0.44484512, 0.51549286, 0.49104759, 0.46871924,\n",
       "       0.49787909, 0.4560545 , 0.5023742 , 0.46178233, 0.51977533,\n",
       "       0.4958794 , 0.507953  , 0.49807175, 0.46868244, 0.48890386,\n",
       "       0.49645554, 0.51261396, 0.4499282 , 0.45799559, 0.4955477 ,\n",
       "       0.5063686 , 0.50223017, 0.44000332, 0.46254431, 0.50857094,\n",
       "       0.47108744, 0.48413566, 0.44832198, 0.50332884, 0.45488223,\n",
       "       0.51421835, 0.50504046, 0.49572537, 0.49857746, 0.50205932,\n",
       "       0.47653219, 0.50322843, 0.48366176, 0.48412068, 0.49136747,\n",
       "       0.47551894, 0.47255961, 0.50191528, 0.51576096, 0.46533463,\n",
       "       0.43993788, 0.5397573 , 0.49016156, 0.48230182, 0.50829969,\n",
       "       0.47628092, 0.50206615, 0.48120314, 0.48443556, 0.51334414,\n",
       "       0.47059671, 0.50983547, 0.44506958, 0.51948726, 0.47669305,\n",
       "       0.48182292, 0.46325451, 0.49528144, 0.48618898, 0.52704394,\n",
       "       0.5087686 , 0.49238572, 0.47845829, 0.48173749, 0.46879967,\n",
       "       0.46542322, 0.49981518, 0.45513167, 0.52915587, 0.51243812,\n",
       "       0.51725311, 0.49210264, 0.44653676, 0.48429152, 0.51311468,\n",
       "       0.46369161, 0.42797334, 0.52859654, 0.51568552, 0.48119315,\n",
       "       0.45789018, 0.47293311, 0.47615869, 0.49463169, 0.48937277,\n",
       "       0.48719723, 0.4999742 , 0.51085372, 0.48445737, 0.46968887,\n",
       "       0.49243434, 0.4922585 , 0.51665015, 0.51781926, 0.53581652,\n",
       "       0.51505076, 0.51574913, 0.4423129 , 0.50405402, 0.52758329,\n",
       "       0.47483056, 0.49443903, 0.5071474 , 0.51241631, 0.51745076,\n",
       "       0.48381262, 0.50564342, 0.48073423, 0.50220835, 0.48263352,\n",
       "       0.50407583, 0.47965237, 0.51572732, 0.5100213 , 0.48094188,\n",
       "       0.49185819, 0.51251172, 0.49761783, 0.49353302, 0.5324787 ,\n",
       "       0.49666819, 0.49237073, 0.50251324, 0.51001631, 0.49887053,\n",
       "       0.50007644, 0.46908591, 0.50715739, 0.50762813, 0.47778489,\n",
       "       0.51412293, 0.49174596, 0.48211598, 0.47314758, 0.51619123,\n",
       "       0.45932872, 0.46722709, 0.49729296, 0.50642222, 0.49820397,\n",
       "       0.4838326 , 0.4922903 , 0.51580275, 0.47719376, 0.53221744])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b170a28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 8: Evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af365c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0010853639379502233"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909dd617",
   "metadata": {},
   "source": [
    "From the above data we get to know that linear regression is not the suitable method to evaluate the above data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5fafaaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we apply logistic regression.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0b04859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_r  = LogisticRegression()\n",
    "log_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7256ba2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_r.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e068517c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_log = log_r.predict(x_test)\n",
    "y_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e3fb194e",
   "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>cgpa</th>\n",
       "      <th>placement_exam_marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>0.434988</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>0.385343</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.300236</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.607565</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>0.676123</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cgpa  placement_exam_marks\n",
       "993  0.434988                  0.21\n",
       "859  0.385343                  0.22\n",
       "298  0.300236                  0.48\n",
       "553  0.607565                  0.17\n",
       "672  0.676123                  0.13"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17b0089a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.54"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c32651bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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