posts/scikit-learn_linear-regression.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":[" # Linear Regression using Scikit-Learn #"]},{"cell_type":"markdown","metadata":{},"source":[" ## Ice Cream Dataset ##\n"," | Temperature C° | Ice Cream Sales |\n"," |:--------------:|:---------------:|\n"," |       15       |        34       |\n"," |       24       |       587       |\n"," |       34       |       1200      |\n"," |       31       |       1080      |\n"," |       29       |       989       |\n"," |       26       |       734       |\n"," |       17       |        80       |\n"," |       11       |        1        |\n"," |       23       |       523       |\n"," |       25       |       651       |"]},{"cell_type":"markdown","metadata":{},"source":[" ### Dependencies ###\n"," Install Numpy for number crunching and Matplotlib for plotting graphs:\n"," ```bash\n"," pip install sklearn\n"," ```"]},{"cell_type":"markdown","metadata":{},"source":[" ### Imports ###"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn import linear_model\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import mean_squared_error\n"]},{"cell_type":"markdown","metadata":{},"source":[" ### Ice Cream Dataset as Numpy Array ###"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["data = np.array([[15, 34],\n","                 [24, 587],\n","                 [34, 1200],\n","                 [31, 1080],\n","                 [29, 989],\n","                 [26, 734],\n","                 [17, 80],\n","                 [11, 1],\n","                 [23, 523],\n","                 [25, 651],\n","                 [0, 0],\n","                 [2, 0],\n","                 [12, 5]])\n"]},{"cell_type":"markdown","metadata":{},"source":[" ### Plotting the Dataset ###"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"image/png":"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n_jobs=None, normalize=False)"},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["regression = linear_model.LinearRegression()\n","regression.fit(x_train, y_train)\n"]},{"cell_type":"markdown","metadata":{},"source":[" ### Predict ###"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["y_prediction = regression.predict(x_test)\n"]},{"cell_type":"markdown","metadata":{},"source":[" ### Plot Predicted Results ###"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX8AAAD4CAYAAAAEhuazAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3df3hU5Zn/8fckE8AQCJmZkJgAbUNgt1BZwIkiXQk/ZrtbdYWil4tFt4KtXqaUS2St0IoXa4TNrmIoCqVb2OgirXZdgdYLV3ZKga35WgNJEHGVBEUhkxDIJNkEgpDM+f6BTJyYAMnMZH6cz+svzpk5Z547T7jnznPO3GMxDMNARERMJSHSAxARkf6n5C8iYkJK/iIiJqTkLyJiQkr+IiImpOQvImJC1kgP4Gp5PJ4+H+twODh9+nQIRxP9zBaz2eIFxWwWwcSclZXV42Oq/EVETEjJX0TEhJT8RURMSMlfRMSElPxFRExIyV9ExISU/EVETEjJX0QkCvl88NxzKRw+bAnL+WPmQ14iImbx1lsDuOsuBwD19R0UFob+NZT8RUSixIULMH36cI4du5ia/zzDw5O2f8S3KQVmzychPTNkr6XkLyISBXbuHMQPfmDzb//ntx8lz/IHfO9/vuOjD/EteTJkbwBa8xcRiaC2NgujR2f6E//06ef4dOVS8ix/CHziqTrYsTVkr6vkLyISIb/6VTK5uddy7tzFVPz739ezdasXmr3dPt9o6n5/X2jZR0SknzU1WRg//lr/9l13naW4uMm/bRlmw+jmOMswWzd7+0aVv4hIP3ruuZSAxP/22ycDEj8As+dD17X99MyL+0MkJJX/mTNn2LhxI8ePH8disfDQQw+RlZVFcXExp06dIj09nSVLlpCSkoJhGJSUlFBRUcHAgQMpKCggJycnFMMQEYla1dWJ5Odn+LcXLWph+fKWbp+bkJ6Jb8mTsGMr1jMttA8eEp13+5SUlDBx4kSWLl1Ke3s7n332Gdu2beO6665jzpw5bN++ne3bt3PPPfdQUVFBXV0d69ato6qqik2bNrF69epQDENEJCrdfPNwPvqoM90ePFiHw+G77DEJ6Znw/aXYwvQFNkEv+5w9e5b//d//ZebMmQBYrVYGDx5MWVkZ+fn5AOTn51NWVgbA/v37mTZtGhaLhbFjx3LmzBkaGxuDHYaISNQpL08iOzvLn/hvG1/Jp0sWYNv+NL5TdREdW9CVf319PUOHDmXDhg188skn5OTkcN9999Hc3ExaWhoAw4YNo7m5GQCv14vD4fAfb7fb8Xq9/ueKiMQ6w4ARIwK/QrHi776LveUIfMjFi7khvm+/t4JO/h0dHXz88ccsXLiQMWPGUFJSwvbt2wOeY7FYsFh615/C7XbjdrsBKCoqCnjD6C2r1RrU8bHIbDGbLV5QzNFq504L3/lOkn97yZIOlmes4Ny+I4FPPFXHwP96ldQlKy97vnDFHHTyt9vt2O12xowZA8CUKVPYvn07qampNDY2kpaWRmNjI0OHDgXAZrMFrF81NDRgs3359iWXy4XL5fJvB7PmpS99jn9mixcUc7Tx+WDkyMBqv6qqluRkg3PP1HZ7zLmTtVy4QjxR+wXuw4YNw2634/F4ADh06BAjRozA6XSyd+9eAPbu3UteXh4ATqeTffv2YRgGR44cITk5WUs+IhLTXnnlmoDEv3JlMzU1HpKTL96t39P9+aG8b7+3QnK3z8KFC1m3bh3t7e0MHz6cgoICDMOguLiY3bt3+2/1BJg0aRLl5eUsXryYAQMGUFBQEIohiIj0u88+g5ycwOr62DEPSUldnjh7Pnz04cUWDZeE+L793rIYhtHdB8mizqW/LPoimv9UDBezxWy2eEExR9rPfpbCv/zLUP/2hg1eZs8+1+PzfZ/35jGavBcr/qu8bz9cyz5q7yAi0gv/938Wvv71awP2nTjh4Ur3tFy6bz9aqL2DiMhVeuyx1IDE/8orp6mpuXLij0aq/EVErqCuLoHrr+9cohk2zMfhw5H9kFawlPxFRC5j/nwbe/YM8m+/+WY93/hGewRHFBpK/iIi3ejaiG3SpPO8/np0XGwOBSV/EZEuujZie/vtk4wc2RHBEYWeLviKiHyuayO2229vo6bGE3eJH1T5i4h024jt0KE6bLbLt12OZar8RcTU3O6BAYn/oYdaqanxxHXiB1X+ImJSl2vEZgaq/EXEdF5+ObAR2z/+Y2AjNjNQ5S8ipnHVjdhMQJW/iJjC2rUpAYl/wwYvNTXmTPygyl9E4lxfG7HFO1X+IhK34qkRW6ip8heRuNO1EZvN1sGhQycjOKLoo+QvInGlayO2XbvqGT8+9huxhVrIkr/P52PZsmXYbDaWLVtGfX09a9eupaWlhZycHH70ox9htVq5cOECzz//PB999BFDhgzh4YcfZvjw4aEahoiYVHW1lfz8zlwSb43YQi1ka/47d+4kOzvbv/3SSy9x66238txzzzF48GB2794NwO7duxk8eDDPPfcct956K1u3bg3VEETEpL75zeEBif/tt08q8V9BSJJ/Q0MD5eXlzJo1CwDDMDh8+DBTpkwBYPr06ZSVlQGwf/9+pk+fDsCUKVN47733iJGvERaRKLN//8VGbMeOXVzEmD37bNw2Ygu1kCz7vPDCC9xzzz20tbUB0NLSQnJyMomJiQDYbDa8Xi8AXq8Xu90OQGJiIsnJybS0tDB06NDuTy4i0oUZG7GFWtDJ/8CBA6SmppKTk8Phw4dDMSYA3G43brcbgKKiIhwOR5/PZbVagzo+FpktZrPFC+aN+U9/Smfu3M5PZi1d2sHq1R2ALXIDC6NwzXPQyf/DDz9k//79VFRUcP78edra2njhhRc4e/YsHR0dJCYm4vV6sdkuTozNZqOhoQG73U5HRwdnz55lyJAhXzqvy+XC5XL5t0+f7vv6ncPhCOr4WGS2mM0WL5gv5s5GbJ2r1ZcascXzjyGYec7KyurxsaDX/L/73e+yceNG1q9fz8MPP8w3vvENFi9ezPjx43n77bcB2LNnD06nE4Drr7+ePXv2APD2228zfvx4LPrEhYhcxq9/nRzQiO3JJ83XiC3Uwnaf//z581m7di0vv/wyX/va15g5cyYAM2fO5Pnnn+dHP/oRKSkpPPzww+EagojEuO4asX3yiQerPqEUNIsRI7faeDyePh9rtj+PwXwxmy1eiP+Y165N4emnO28E+fnPvSxcmBLXMXcnXMs+ev8Ukahy+UZsKREZUzxSYzcRiRo//nFgI7bf/EaN2MJFlb+IRJwasfU/JX8RiajvftfG3r1qxNbflPxFJCK6NmKbPPk8v/uduS7mRpKSv4j0u29+c7i/Hw9cbMSmfjz9Sxd8RaTfdG3ENmeOGrFFiip/EQk7NWKLPqr8RSSsdu0aGJD4CwpaqKnxKPFHmCp/EQmLjg4YNSqw2q+uruWaa2KiqUDcU+UvIiH3q18lByT+S43YlPijhyp/EQmZc+dg9Gg1YosFqvxFJCSKi1MCEv/Pf+6lpkaJP1ppWkQkKM3NFsaN66kRm0QrVf4i0mc//nFqQOL/j/9QI7ZYocpfRHqttjYBp7OzEZvd3sG776oRWyxR8heRXlEjtvgQdPI/ffo069evp6mpCYvFgsvl4pZbbqG1tZXi4mJOnTpFeno6S5YsISUlBcMwKCkpoaKigoEDB1JQUEBOTk4oYhGRMKqqsjJ9uhqxxYugk39iYiL33nsvOTk5tLW1sWzZMiZMmMCePXu47rrrmDNnDtu3b2f79u3cc889VFRUUFdXx7p166iqqmLTpk2sXr06FLGISJioEVv8CfqCb1pamr9yv+aaa8jOzsbr9VJWVkZ+fj4A+fn5lJWVAbB//36mTZuGxWJh7NixnDlzhsbGxmCHISJhUFamRmzxKqRr/vX19Xz88cfk5ubS3NxMWloaAMOGDaO5uRkAr9eLw+HwH2O32/F6vf7nXuJ2u3G73QAUFRUFHNNbVqs1qONjkdliNlu8EN6YDQMGDRoQsM/jOY/dbgUi93PWPIfwvKE60blz51izZg333XcfycnJAY9ZLBYsvbz3y+Vy4XK5/Nt9/fZ6AIfDEdTxschsMZstXghfzLt2DWTBArt/e9GiFpYvb8EwINI/Ys1z72RlZfX4WEiSf3t7O2vWrOHmm2/mxhtvBCA1NZXGxkbS0tJobGxk6NChANhstoBAGhoasNlsoRiGiARBjdjMJeg1f8Mw2LhxI9nZ2dx2223+/U6nk7179wKwd+9e8vLy/Pv37duHYRgcOXKE5OTkLy35iEj/6tqI7amnmtSILc4FXfl/+OGH7Nu3j1GjRvHoo48CcPfddzNnzhyKi4vZvXu3/1ZPgEmTJlFeXs7ixYsZMGAABQUFwQ5BRPpIjdjMy2IYRky8tXs8nj4fq3XC+Ge2eCH4mIuLU3jmmaH+7Y0bvfzt354LxdDCRvPcO2Ff8xeR2KFGbAJq7CZiKo8+qkZscpEqfxETUCM26UrJXyTOzZtn53/+Z6B/+7//u55x49SIzeyU/EXiVNdGbE7neXbsMNfFUumZkr9IHLrppuF8+mnnf+8//ekkI0aoH4900gVfkThyqRHbpcT/ne9cbMSmxC9dqfIXiQOGASNGBN7TfehQLTZbTHyMRyJAlb9IjNu1a2BA4l+0qIWaGo8Sv1yWKn+RGNXRAdnZasQmfaPKXyQGbd2aTHJyZ799NWKT3lLlLxJD1IhNQkWVv0iMePbZlIDE/+tfX6CmRolf+ka/NiJRrqdGbOnpjoh/s5bELlX+IlGsayO2V19VIzYJDVX+IlGoayO29PQOKivViE1CR8lfJMr83d/Z+eMf1YhNwitiyb+yspKSkhJ8Ph+zZs1izpw5IX8N36k62LEV75kWfI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y_test)\n","plt.plot(x_test, y_prediction, color='blue')\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":[" ### Print Metrics ###"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Coefficient: \n [[60.96610169]]\nIntercept: \n [-844.45762712]\nMean Squared Error: 253509.50\n"}],"source":["print('Coefficient: \\n', regression.coef_)\n","print('Intercept: \\n', regression.intercept_)\n","print('Mean Squared Error: %.2f' % mean_squared_error(y_test, y_prediction))\n"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3}},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3}}