Part-2.1-SimpleLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 : Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "\n",
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n",
    "\n",
    "print(\"------( X_train )------\")\n",
    "print(pd.DataFrame(X_train))\n",
    "print(\"------( y_train )------\")\n",
    "print(pd.DataFrame(y_train))\n",
    "print(\"------( X_test )------\")\n",
    "print(pd.DataFrame(X_test))\n",
    "print(\"------( y_test )------\")\n",
    "print(pd.DataFrame(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### do we need feature scaling here?\n",
    "### do we need one hot encoding here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Simple Linear Regression to the Training set\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "print(\"------( X_test | year of experience )------\")\n",
    "print(pd.DataFrame(X_test))\n",
    "\n",
    "print(\"------( y_pred | predicted salaries)------\")\n",
    "print(pd.DataFrame(y_pred))\n",
    "\n",
    "print(\"------( y_test | actual salaries )------\")\n",
    "print(pd.DataFrame(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising the Training set results\n",
    "\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Salary vs Experience (Training set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising the Test set results\n",
    "\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Salary vs Experience (Test set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display the equation\n",
    "\n",
    "print(\"Value of coeffient is {}\".format(regressor.coef_[0]))\n",
    "print(\"Value of intercept is {}\".format(regressor.intercept_))\n",
    "print(\"Linear equation ====> Y = {} * X + {}\".format(regressor.coef_[0],regressor.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_salary(experience):\n",
    "    salary = regressor.predict(np.array(experience).reshape(1,1))[0]\n",
    "    msg = \"Salary for {} years of experience should be {}\".format(experience,salary)\n",
    "    return msg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_salary(23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### we will cover this post model performance evaluation lecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model evaluaation\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r_sqare = r2_score(y_test, y_pred)\n",
    "print(\"R-Square value is : {:.2f} %\".format(r_sqare*100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}