LinearRegressionFromScratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "stock-arizona",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "unlikely-sixth",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 20, 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "above-thomas",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Fish.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "innovative-tiffany",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bream</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>34.7</td>\n",
       "      <td>13.6024</td>\n",
       "      <td>4.9274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bream</td>\n",
       "      <td>500.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>14.1795</td>\n",
       "      <td>5.2785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bream</td>\n",
       "      <td>390.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.6700</td>\n",
       "      <td>4.6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bream</td>\n",
       "      <td>450.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>14.0049</td>\n",
       "      <td>4.8438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bream</td>\n",
       "      <td>500.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.2</td>\n",
       "      <td>14.2266</td>\n",
       "      <td>4.9594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species  Weight  Length1  Length2  Length3   Height   Width\n",
       "0   Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
       "1   Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
       "2   Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
       "3   Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
       "4   Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
       "5   Bream   450.0     26.8     29.7     34.7  13.6024  4.9274\n",
       "6   Bream   500.0     26.8     29.7     34.5  14.1795  5.2785\n",
       "7   Bream   390.0     27.6     30.0     35.0  12.6700  4.6900\n",
       "8   Bream   450.0     27.6     30.0     35.1  14.0049  4.8438\n",
       "9   Bream   500.0     28.5     30.7     36.2  14.2266  4.9594"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "durable-graphic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Species  159 non-null    object \n",
      " 1   Weight   159 non-null    float64\n",
      " 2   Length1  159 non-null    float64\n",
      " 3   Length2  159 non-null    float64\n",
      " 4   Length3  159 non-null    float64\n",
      " 5   Height   159 non-null    float64\n",
      " 6   Width    159 non-null    float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 8.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "interracial-duplicate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>398.326415</td>\n",
       "      <td>26.247170</td>\n",
       "      <td>28.415723</td>\n",
       "      <td>31.227044</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.417486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>357.978317</td>\n",
       "      <td>9.996441</td>\n",
       "      <td>10.716328</td>\n",
       "      <td>11.610246</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.685804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>1.728400</td>\n",
       "      <td>1.047600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>5.944800</td>\n",
       "      <td>3.385650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>273.000000</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>7.786000</td>\n",
       "      <td>4.248500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>650.000000</td>\n",
       "      <td>32.700000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>12.365900</td>\n",
       "      <td>5.584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>18.957000</td>\n",
       "      <td>8.142000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weight     Length1     Length2     Length3      Height       Width\n",
       "count   159.000000  159.000000  159.000000  159.000000  159.000000  159.000000\n",
       "mean    398.326415   26.247170   28.415723   31.227044    8.970994    4.417486\n",
       "std     357.978317    9.996441   10.716328   11.610246    4.286208    1.685804\n",
       "min       0.000000    7.500000    8.400000    8.800000    1.728400    1.047600\n",
       "25%     120.000000   19.050000   21.000000   23.150000    5.944800    3.385650\n",
       "50%     273.000000   25.200000   27.300000   29.400000    7.786000    4.248500\n",
       "75%     650.000000   32.700000   35.500000   39.650000   12.365900    5.584500\n",
       "max    1650.000000   59.000000   63.400000   68.000000   18.957000    8.142000"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "north-mission",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "suburban-welding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f794efb6b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f794ef36970>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f794eeed250>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f794ef15ac0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f794eecc370>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f794ee75bb0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "renewable-maker",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "incomplete-mattress",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight']=df['Weight'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "undefined-webcam",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Length1']=df['Length1'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "lightweight-nightlife",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Height']=df['Height'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "utility-albany",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Width']=df['Width'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "experienced-durham",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Length2']=df['Length2'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "medium-winning",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Length3']=df['Length3'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "essential-valuable",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(X):\n",
    "    X = X.sample(frac=1)\n",
    "    TRAIN_SIZE = int(0.8 * len(X))\n",
    "    train, test = X.iloc[:TRAIN_SIZE, :], X.iloc[TRAIN_SIZE:, :]\n",
    "\n",
    "    train_y, test_y = train.pop(\"Weight\"), test.pop(\"Weight\")\n",
    "\n",
    "    return (train.to_numpy(), test.to_numpy(), train_y.to_numpy(), test_y.to_numpy())\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "federal-superior",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearReg:\n",
    "    def __init__(self):\n",
    "        self.coefs = np.random.randn(1, len(x_train[0])) # random vals for each parameter coeff\n",
    "        self.slope = 0 # slope starts at 0\n",
    "        self.predictions = []\n",
    "\n",
    "    def forward_prop(self, X):\n",
    "        # matrix multiplication between coeffs and X\n",
    "        # X.shape = n * m   coefs.shape = 1 * n     z.shape = 1 * m\n",
    "        z = np.dot(self.coefs, X) + self.slope\n",
    "        self.predictions = z\n",
    "        # adding the bias AKA slope\n",
    "        # z --> predicted vals\n",
    "        return z\n",
    "    \n",
    "    def cost_func(self, z, y):\n",
    "        # z-> predicted y-> truth\n",
    "        m = len(y) # m vals (number of vals)\n",
    "        J = (1/(2 * m)) * np.sum(np.square(z - y)) # getting squared errors\n",
    "        # multiplying by reciprocal of 2m for cost func\n",
    "        return J\n",
    "\n",
    "    def back_prop(self, X, y, z):\n",
    "        m = len(y) # m vals (number of vals)\n",
    "        d_coeffs =  (-2 / m) * np.sum((y - z) * X) #gradient of coeffs\n",
    "        d_slope = (-2 / m) * np.sum((y - z)) #gradient of slopee\n",
    "        return d_coeffs, d_slope\n",
    "    \n",
    "    def gradient_descent_update(self, d_coeffs, d_slope, lr):\n",
    "        #updating gradients\n",
    "        self.coefs = self.coefs - (lr * d_coeffs)\n",
    "        self.slope = self.slope - (lr * d_slope)\n",
    "    \n",
    "    def normalise_vals(self, arr):\n",
    "        arr = arr - arr.mean(axis=0)\n",
    "        arr = arr / np.abs(arr).max(axis=0)\n",
    "        return arr\n",
    "\n",
    "    def fit(self, x_train, y_train, x_test, y_test, lr, epochs):\n",
    "        \"\"\"\n",
    "        given X and y fitting a linear reg\n",
    "        \"\"\"\n",
    "        x_train = self.normalise_vals(x_train)\n",
    "        x_test = self.normalise_vals(x_test)\n",
    "\n",
    "        cost_train_vals = []\n",
    "        cost_test_vals = []\n",
    "\n",
    "        MAE_train_vals = []\n",
    "        MAE_test_vals = []\n",
    "\n",
    "        m_train = len(y_train)\n",
    "        m_val = len(y_test)\n",
    "\n",
    "        for epoch in range(1, epochs + 1):\n",
    "            z_train = self.forward_prop(x_train)\n",
    "            cost_train = self.cost_func(z_train, y_train)\n",
    "            dw, db = self.back_prop(x_train, y_train, z_train)\n",
    "            self.gradient_descent_update(dw, db, lr)\n",
    "\n",
    "            #Mean absolute error MAE\n",
    "            MAE_train = (1 / m_train) * np.sum(np.abs(z_train - y_train))\n",
    "\n",
    "            #validation set\n",
    "            z_val = self.forward_prop(x_test)\n",
    "            cost_val = self.cost_func(z_val, y_test)\n",
    "            MAE_val = (1 / m_val) * np.sum(np.abs(z_val - y_test))\n",
    "            \n",
    "            #storing vals for graphing\n",
    "            cost_train_vals.append(cost_train)\n",
    "            cost_test_vals.append(cost_val)\n",
    "\n",
    "            MAE_train_vals.append(MAE_train)\n",
    "            MAE_test_vals.append(MAE_val)\n",
    "\n",
    "            #prinitng out\n",
    "            if epoch % 10 == 0:\n",
    "                print(f\"EPOCHS {epoch} / {epochs} training MAE {MAE_train} validation MAE {MAE_val} training cost {cost_train} validation cost {cost_val}\")\n",
    "        \n",
    "        # plotting graph\n",
    "        fig, ax = plt.subplots(1, 2)\n",
    "        \n",
    "        ax[0].plot(cost_train_vals)\n",
    "        ax[0].scatter([x for x in range(len(cost_train_vals))], cost_train_vals, label=\"train_set\")\n",
    "        ax[0].scatter([x for x in range(len(cost_test_vals))], cost_test_vals, label=\"test_set\")\n",
    "        ax[0].plot(cost_test_vals)\n",
    "        ax[0].title.set_text('Cost')\n",
    "        ax[0].legend()\n",
    "\n",
    "        ax[1].plot(MAE_train_vals)\n",
    "        ax[1].scatter([x for x in range(len(MAE_train_vals))], MAE_train_vals, label=\"train_set\")\n",
    "        ax[1].scatter([x for x in range(len(MAE_test_vals))], MAE_test_vals, label=\"test_set\")\n",
    "        ax[1].plot(MAE_test_vals)\n",
    "        ax[1].title.set_text('MAE')\n",
    "        ax[1].legend()\n",
    "\n",
    "\n",
    "        return (self.slope, self.coefs)\n",
    "    \n",
    "    def R2_val (self, X, y):\n",
    "        m = len(y)\n",
    "        z = np.dot(self.coefs, X.T) + self.slope\n",
    "\n",
    "        # variation across line\n",
    "        ss = np.sum((y - z) **2)\n",
    "        ss /= m\n",
    "\n",
    "        # variation across mean\n",
    "        mean = np.mean(y)\n",
    "        var_mean = np.sum((y - mean) ** 2)\n",
    "        var_mean /= m \n",
    "\n",
    "        print(f\"var across line {ss} || var across mean {var_mean}\")\n",
    "        r2 = 1 - ss / var_mean\n",
    "        return abs(r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "express-arnold",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCHS 10 / 50 training MAE 323.9906977654902 validation MAE 434.20174285775016 training cost 108558.52701566288 validation cost 161648.03670430134\n",
      "EPOCHS 20 / 50 training MAE 287.8190944658211 validation MAE 399.0620168278002 training cost 92211.12965928018 validation cost 139995.87803671538\n",
      "EPOCHS 30 / 50 training MAE 272.19822017309286 validation MAE 376.31138869855977 training cost 81297.47686671096 validation cost 124651.09193212443\n",
      "EPOCHS 40 / 50 training MAE 267.104647323805 validation MAE 361.0683699779282 training cost 74011.43526142449 validation cost 113679.89598420422\n",
      "EPOCHS 50 / 50 training MAE 264.89398804895814 validation MAE 351.48428520956196 training cost 69147.21580319596 validation cost 105761.51244593697\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linearReg = MyLinearReg()\n",
    "LEARNING_RATE = 0.01\n",
    "EPOCHS = 50\n",
    "slope, coeff = linearReg.fit(x_train.T, y_train, x_test.T, y_test, LEARNING_RATE, EPOCHS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "floral-enclosure",
   "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>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>398.326415</td>\n",
       "      <td>26.247170</td>\n",
       "      <td>28.415723</td>\n",
       "      <td>31.227044</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.417486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>357.978317</td>\n",
       "      <td>9.996441</td>\n",
       "      <td>10.716328</td>\n",
       "      <td>11.610246</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.685804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>1.728400</td>\n",
       "      <td>1.047600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>5.944800</td>\n",
       "      <td>3.385650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>273.000000</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>7.786000</td>\n",
       "      <td>4.248500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>650.000000</td>\n",
       "      <td>32.700000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>12.365900</td>\n",
       "      <td>5.584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>18.957000</td>\n",
       "      <td>8.142000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weight     Length1     Length2     Length3      Height       Width\n",
       "count   159.000000  159.000000  159.000000  159.000000  159.000000  159.000000\n",
       "mean    398.326415   26.247170   28.415723   31.227044    8.970994    4.417486\n",
       "std     357.978317    9.996441   10.716328   11.610246    4.286208    1.685804\n",
       "min       0.000000    7.500000    8.400000    8.800000    1.728400    1.047600\n",
       "25%     120.000000   19.050000   21.000000   23.150000    5.944800    3.385650\n",
       "50%     273.000000   25.200000   27.300000   29.400000    7.786000    4.248500\n",
       "75%     650.000000   32.700000   35.500000   39.650000   12.365900    5.584500\n",
       "max    1650.000000   59.000000   63.400000   68.000000   18.957000    8.142000"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "floating-satin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(239.17763102979356,\n",
       " array([[ 0.43680757,  0.29248651, -1.74723084, -1.13083673, -0.99469519]]))"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " slope, coeff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "seven-structure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var across line 255049.47551292466 || var across mean 152521.91561523438\n",
      "--------------------\n",
      "R SQUARED = 0.6722152648301087\n"
     ]
    }
   ],
   "source": [
    "r2 = linearReg.R2_val(x_test, y_test)\n",
    "print(\"-\"*20)\n",
    "print(f\"R SQUARED = {r2}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "black-welsh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter([x for x in range(y_test.shape[0])], y_test, label=\"actual\")\n",
    "ax.scatter([x for x in range(linearReg.predictions.shape[1])], linearReg.predictions, label = \"predictions\")\n",
    "ax.legend()\n",
    "plt.title(\"Actual  VS predicted\")\n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "synthetic-going",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression().fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "endless-gentleman",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-489.0096632239906\n",
      "[ 67.88346422 -10.7507045  -29.56369354  28.46849806  16.50488414]\n"
     ]
    }
   ],
   "source": [
    " print(f\"{model.intercept_}\\n{model.coef_}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "referenced-announcement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model score (R squared) val with training set 0.8879590329135785\n",
      "----------------------------------------\n",
      "Model score (R squared) val with testing set 0.8674518406443346\n"
     ]
    }
   ],
   "source": [
    " print(f\"Model score (R squared) val with training set {model.score(x_train, y_train)}\")\n",
    "print(\"--\"*20)\n",
    "print(f\"Model score (R squared) val with testing set {model.score(x_test, y_test)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "environmental-senior",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "contrary-stock",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "spectacular-cheese",
   "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>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>101</td>\n",
       "      <td>116</td>\n",
       "      <td>93</td>\n",
       "      <td>124</td>\n",
       "      <td>154</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Perch</td>\n",
       "      <td>300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2.2139</td>\n",
       "      <td>3.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Species Weight Length1 Length2 Length3  Height  Width\n",
       "count      159    159     159     159     159     159    159\n",
       "unique       7    101     116      93     124     154    152\n",
       "top      Perch  300.0    19.0    22.0    23.5  2.2139  3.525\n",
       "freq        56      6       6       7       5       2      3"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "confidential-broad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Species  159 non-null    object\n",
      " 1   Weight   159 non-null    object\n",
      " 2   Length1  159 non-null    object\n",
      " 3   Length2  159 non-null    object\n",
      " 4   Length3  159 non-null    object\n",
      " 5   Height   159 non-null    object\n",
      " 6   Width    159 non-null    object\n",
      "dtypes: object(7)\n",
      "memory usage: 8.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "steady-referral",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "flush-mistake",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "political-angel",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "partial-monte",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "behind-singing",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "amino-pencil",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "amazing-township",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "characteristic-basin",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "happy-stroke",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "radio-parent",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bronze-portsmouth",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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