Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression\n",
    "\n",
    "* Initalize the class with Learning Rate and Number of Iterations to run for.\n",
    "* Initalize the weights (based on features) and bias.\n",
    "* Build the objective function and find ypred.\n",
    "* Using Loss Function (MSE or MAE), find the error.\n",
    "* Gradient Descent Algorithm on Objective Function\n",
    "* Update weights based on GD\n",
    "* Repeat from step 3, until the objective function is converged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    def __init__(self, lr, iters=100):\n",
    "        self.lr = lr\n",
    "        self.iters = iters\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "        \n",
    "        for _ in range(self.iters):\n",
    "            y_predict = np.dot(X, self.weights) + self.bias\n",
    "\n",
    "            dw = (1/n_samples) * np.dot(X.T, (y_predict - y))\n",
    "            db = (1/n_samples) * np.sum(y_predict - y)\n",
    "\n",
    "            self.weights -= self.lr * dw\n",
    "            self.bias -= self.lr * db\n",
    "        \n",
    "    def predict(self, X):\n",
    "        y_predict = np.dot(X, self.weights) +self.bias\n",
    "        return y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 305.77413160554664\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def mean_squared_error(y_true, y_pred):\n",
    "    return np.mean((y_true - y_pred)**2)\n",
    "\n",
    "X, y = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
    "\n",
    "regressor = LinearRegression(lr=0.01, iters=2000)\n",
    "regressor.fit(X_train, y_train)\n",
    "predictions = regressor.predict(X_test)\n",
    "    \n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "print(\"MSE:\", mse)\n",
    "\n",
    "y_pred_line = regressor.predict(X)\n",
    "cmap = plt.get_cmap('viridis')\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "m1 = plt.scatter(X_train, y_train, color=cmap(0.9), s=10)\n",
    "m2 = plt.scatter(X_test, y_test, color=cmap(0.5), s=10)\n",
    "plt.plot(X, y_pred_line, color='black', linewidth=2, label=\"Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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