diff --git a/Predictions/Numeral_Regression.ipynb b/Predictions/Numeral_Regression.ipynb deleted file mode 100644 index e27fdc6..0000000 --- a/Predictions/Numeral_Regression.ipynb +++ /dev/null @@ -1,292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1732817d", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2c67c4f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ResultsReachImpressionsVideo_playsLink_clicksEngagementAmount_Spent
015341534153514480622
1859385931059902220
214057157245701405
313139614790134723
43761650924013539568035711133
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" - ], - "text/plain": [ - " Results Reach Impressions Video_plays Link_clicks Engagement \\\n", - "0 1534 1534 1535 1448 0 62 \n", - "1 8593 8593 10599 0 2 2 \n", - "2 140 571 572 457 0 140 \n", - "3 13 1396 1479 0 13 47 \n", - "4 37616 5092 40135 39568 0 35711 \n", - "\n", - " Amount_Spent \n", - "0 2 \n", - "1 20 \n", - "2 5 \n", - "3 23 \n", - "4 133 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = {'Results': [1534,8593,140,13,37616,1060,694,64,17744],\n", - " 'Reach': [1534,8593,571,1396,5092,6933,2008,2825,6154],\n", - " 'Impressions': [1535,10599,572,1479,40135,11468,2435,5087,21332],\n", - " 'Video_plays': [1448,0,457,0,39568,0,1225,0,20905],\n", - " 'Link_clicks': [0,2,0,13,0,100,1,49,0],\n", - " 'Engagement': [62,2,140,47,35711,1060,694,145,15604],\n", - " 'Amount_Spent': [2,20,5,23,133,89,37,85,76]}\n", - "\n", - "df = pd.DataFrame(data)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "096de0cb", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import mean_squared_error, r2_score" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "00517f34", - "metadata": {}, - "outputs": [], - "source": [ - "# X contains the features (Results, Reach, Impressions, Video Plays, Link clicks, and Post engagement)\n", - "X = df[['Results', 'Reach', 'Impressions', 'Video_plays', 'Link_clicks', 'Engagement']]\n", - "\n", - "# y contains the target variable (Amount spent)\n", - "y = df['Amount_Spent']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a56a0001", - "metadata": {}, - "outputs": [], - "source": [ - "# Split dataset\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e54736e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression()" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Linear Regression model\n", - "model = LinearRegression()\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2b5aa068", - "metadata": {}, - "outputs": [], - "source": [ - "# Predict model using X_test\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f9eb2f9e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error: 10546.18825415638\n", - "R-squared: -8.984556927011957\n" - ] - } - ], - "source": [ - "# Evaluate model performance\n", - "mse = mean_squared_error(y_test, y_pred)\n", - "r2 = r2_score(y_test, y_pred)\n", - "\n", - "print(\"Mean Squared Error:\", mse)\n", - "print(\"R-squared:\", r2)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0430cf6c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicted amount spent: -34.41443487262584\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sang.yogi\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Predict the amount spent for new data\n", - "X_new = [[100, 2000, 5000, 1000, 50, 150]]\n", - "predicted_amount_spent = model.predict(X_new)\n", - "print(\"Predicted amount spent:\", predicted_amount_spent[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d17c4a06", - "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": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}