{"id":146662,"date":"2025-08-22T11:58:51","date_gmt":"2025-08-22T06:28:51","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146662"},"modified":"2025-08-22T11:58:51","modified_gmt":"2025-08-22T06:28:51","slug":"used-car-price-prediction-using-ann","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/used-car-price-prediction-using-ann\/","title":{"rendered":"Deep Learning Project &#8211; Used Car Price Prediction using ANN"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1obbM3RmtRvNCP4XpE_ldghFnzz5qEtZp\/view?usp=sharing\"><strong>Car Details Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">Used Car Price Prediction Using Vehicle Specifications and Artificial Neural Networks\r\n\r\nTo develop a predictive model using Artificial Neural Networks (ANN) that estimates the selling price of a used car based on its features such as year, mileage, fuel type, transmission, and model. The aim is to help customers, dealers, and resellers make data-driven decisions in pricing and purchasing used cars.\r\n\r\n\r\n# Step 1: Import Libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\n\r\n# Step 2: Load Dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/car\/CAR DETAILS FROM CAR DEKHO.csv\")\r\ndf.shape\r\n\r\n# Step 3: Encode Categorical Columns\r\ndf['fuel'] = LabelEncoder().fit_transform(df['fuel'])\r\ndf['seller_type'] = LabelEncoder().fit_transform(df['seller_type'])\r\ndf['transmission'] = LabelEncoder().fit_transform(df['transmission'])\r\ndf['owner'] = LabelEncoder().fit_transform(df['owner'])\r\ndf.head()\r\n\r\n# Step 4: Features &amp; Target\r\nX = df.drop(['name','selling_price'], axis=1) # Input value\r\ny = df['selling_price'] # Output value\r\ny.head()\r\n\r\n# Step 5: Normalize\r\nscaler = StandardScaler()\r\nX_scaled = scaler.fit_transform(X)\r\n\r\n# Step 6: Train-Test Split\r\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\r\nlen(X_test)\r\n\r\n# Step 7: Build ANN Model\r\nmodel = Sequential([\r\n    Dense(64, activation='relu', input_shape=(X_train.shape[1],)), # input+hidden\r\n    Dense(32, activation='relu'), #hidden\r\n    Dense(16, activation='relu'), #hidden\r\n    Dense(1) #out put\r\n])\r\nmodel.compile(optimizer='adam', loss='mse')\r\n\r\n# Step 8: Train\r\nhistory = model.fit(X_train, y_train, epochs=100, batch_size=10, verbose=1)\r\n\r\n# Step 9: Predict &amp; Evaluate\r\ny_pred = model.predict(X_test).flatten()\r\nprint(\"MSE:\", mean_squared_error(y_test, y_pred))\r\nprint(\"R2 Score:\", r2_score(y_test, y_pred))\r\n\r\n#Step 10: Plot Training Loss\r\nplt.plot(history.history['loss'])\r\nplt.title(\"Training Loss Over Epochs\")\r\nplt.xlabel(\"Epoch\")\r\nplt.ylabel(\"Loss (MSE)\")\r\nplt.grid()\r\nplt.show()\r\n\r\n# Step 11: Actual vs Predicted\r\nplt.figure(figsize=(6,6))\r\nsns.scatterplot(x=y_test, y=y_pred,color=\"blue\", alpha=0.7, label=\"Predicted\")\r\nplt.xlabel(\"Actual Price\")\r\nplt.ylabel(\"Predicted Price\")\r\nplt.title(\"Actual vs Predicted Used Car Prices\")\r\nplt.grid()\r\nplt.show()\r\n\r\nnew_data = np.array([[2017,79000,2,1,1,1]])\r\nnew_data_scaled = scaler.transform(new_data)\r\ny_pred = model.predict(new_data_scaled)\r\ny_pred\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:12,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1obbM3RmtRvNCP4XpE_ldghFnzz5qEtZp\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Car Details Dataset Used Car Price Prediction Using Vehicle Specifications and Artificial Neural Networks To develop a predictive model using Artificial Neural Networks (ANN) that estimates the selling price of a used&#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":[35114,8431,33127,33128,20697,35115,35111,35113,35112,35116],"class_list":["post-146662","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-deep-learning-used-car-price-prediction-using-ann","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-used-car-price-prediction","tag-used-car-price-prediction-using-ann","tag-used-car-price-prediction-using-deep-learning","tag-used-car-price-prediction-using-machine-learning","tag-used-car-price-prediction-using-vehicle-specifications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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