Code-Example-011-Linear-Regression-Residual-Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "\n",
    "Is there an association between the number of hours spent studying and the performance on the final exam? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   age     100 non-null    int64\n",
      " 1   height  100 non-null    int64\n",
      " 2   salary  100 non-null    int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 2.5 KB\n"
     ]
    }
   ],
   "source": [
    "ceo_df = pd.read_csv(\"./datasets/CEO_salary.csv\")\n",
    "ceo_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>salary</th>\n",
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       "      <th>0</th>\n",
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       "      <td>518889</td>\n",
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       "      <th>1</th>\n",
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       "      <td>496345</td>\n",
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       "      <th>3</th>\n",
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       "      <th>95</th>\n",
       "      <td>60</td>\n",
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       "      <td>508847</td>\n",
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       "      <th>96</th>\n",
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       "      <td>78</td>\n",
       "      <td>503887</td>\n",
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       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  height  salary\n",
       "0    57      74  518889\n",
       "1    64      74  536408\n",
       "2    52      70  496345\n",
       "3    63      64  508838\n",
       "4    29      75  451312\n",
       "..  ...     ...     ...\n",
       "95   60      67  508847\n",
       "96   60      72  521384\n",
       "97   35      64  438752\n",
       "98   27      70  433771\n",
       "99   47      78  503887\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062864</td>\n",
       "      <td>0.929197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
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       "      <td>1.000000</td>\n",
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       "    </tr>\n",
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       "      <th>salary</th>\n",
       "      <td>0.929197</td>\n",
       "      <td>0.310440</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "             age    height    salary\n",
       "age     1.000000 -0.062864  0.929197\n",
       "height -0.062864  1.000000  0.310440\n",
       "salary  0.929197  0.310440  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See Pandas Documentation for pandas correlation calculation. \n",
    "# https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html?highlight=corr\n",
    "ceo_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = ceo_df[\"age\"]\n",
    "height = ceo_df[\"height\"]\n",
    "salary = ceo_df[\"salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Prepare the data in higher dimentions, List of Lists, Array of Arrays\n",
    "my_X = np.vstack((age,height, )).T\n",
    "print(my_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2502.99561142 2507.03232683]\n",
      "190697.2913678494\n"
     ]
    }
   ],
   "source": [
    "reg = LinearRegression().fit(my_X, salary)\n",
    "\n",
    "# print(dir(reg))\n",
    "print(reg.coef_)\n",
    "print(reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999986393414"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(my_X, salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install yellowbrick package \n",
    "# ! pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7a04002b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from yellowbrick.regressor import ResidualsPlot\n",
    "\n",
    "\n",
    "# Create the train and test data\n",
    "# X_train, X_test, y_train, y_test = train_test_split(my_X, salary, test_size=0.3, random_state=123)\n",
    "\n",
    "# Instantiate the linear model and visualizer\n",
    "model = LinearRegression()\n",
    "visualizer = ResidualsPlot(model)\n",
    "\n",
    "\n",
    "visualizer.fit(my_X, salary)  # Fit the training data to the visualizer\n",
    "# visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.show()                 # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}