{"id":145677,"date":"2025-06-27T17:06:29","date_gmt":"2025-06-27T11:36:29","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145677"},"modified":"2025-06-27T17:06:29","modified_gmt":"2025-06-27T11:36:29","slug":"sales-forecasting-for-a-retail-store-using-multiple-variable","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sales-forecasting-for-a-retail-store-using-multiple-variable\/","title":{"rendered":"ML Project &#8211; Sales Forecasting for a Retail Store using Multiple Variable for Linear Regression"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><strong><a href=\"https:\/\/drive.google.com\/file\/d\/1boPYvsVjHedMOli9n63LBMhU7FOusns4\/view?usp=sharing\">Retail Sales Dataset<\/a><\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># Project Sales Forecasting for a Retail Store\r\n# To predict future sales for a retail store based on historical sales data\r\n# using a Linear Regression model.\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\nimport seaborn as sns\r\n\r\n# Load dataset\r\ndf = pd.read_csv(\"retail_sales.csv\")\r\n\r\n# Display basic info\r\n#print(df.head())\r\n#print(\"\\nCorrelation Matrix:\\n\", df.corr())\r\n\r\n# Visualize\r\n# sns.pairplot(df)\r\n# plt.show()\r\n\r\n# Independed and Depended variables\r\nX = df[['Advertising_Budget', 'Customers_Visited']] # Independed variable\r\ny = df['Sales'] # Depended variables\r\n\r\n# x_train---&gt; Training data set for Independed variable\r\n# y_train---&gt; Training data set for depended variable\r\n# x_test---&gt; Testing data set for Independed variable\r\n# y_test---&gt; Testing data set for depended variable\r\n\r\n# Train-Test Split\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,)\r\n# print(len(X_train))\r\n# print(len(y_train))\r\n\r\n# Linear Regression Model\r\nmodel = LinearRegression()\r\nmodel.fit(X_train, y_train)\r\n#print(model)\r\n# Predictions\r\ny_pred = model.predict(X_test)\r\n#print(y_pred)\r\n# Evaluation\r\n#print(model.score(X_test,y_test))\r\nprint(\"\\nModel Coefficients:\", model.coef_)\r\nprint(\"Model Intercept:\", model.intercept_)\r\nprint(\"Mean Squared Error:\", mean_squared_error(y_test, y_pred))\r\nprint(\"R\u00b2 Score:\", r2_score(y_test, y_pred))\r\n\r\n#Plot Actual vs Predicted\r\nplt.scatter(y_test, y_pred, color='blue')\r\nplt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linewidth=2)\r\nplt.xlabel('Actual Sales')\r\nplt.ylabel('Predicted Sales')\r\nplt.title('Actual vs Predicted Sales')\r\nplt.grid(True)\r\nplt.show()<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:45,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1boPYvsVjHedMOli9n63LBMhU7FOusns4\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205112538\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1boPYvsVjHedMOli9n63LBMhU7FOusns4\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 20:13:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-13 22:25:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-24 14:15:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-29 15:40:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-29 11:16:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-05 06:32:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 07:36:33&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-12 20:34:27&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-03 14:46:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-21 06:01:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-26 22:54:54&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-04-26 22:54:54&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Retail Sales Dataset # Project Sales Forecasting for a Retail Store # To predict future sales for a retail store based on historical sales data # using a Linear Regression model. import&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[8431,33127,33128,20697,34751,34753,34752,34750],"class_list":["post-145677","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-sales-forecasting-for-a-retail-store","tag-sales-forecasting-for-a-retail-store-using-linear-regression-for-multiple-variable","tag-sales-forecasting-for-a-retail-store-using-multiple-variable","tag-sales-forecasting-for-a-retail-store-using-multiple-variable-for-linear-regression"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ML Project - Sales Forecasting for a Retail Store using Multiple Variable for Linear Regression - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/sales-forecasting-for-a-retail-store-using-multiple-variable\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML Project - Sales Forecasting for a Retail Store using Multiple Variable for Linear Regression - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Retail Sales Dataset # 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