{"id":10371,"date":"2023-01-18T19:26:13","date_gmt":"2023-01-18T13:56:13","guid":{"rendered":"http:\/\/myprojectideas.com\/?p=10371"},"modified":"2025-10-15T11:45:28","modified_gmt":"2025-10-15T11:45:28","slug":"car-price-prediction-machine-learning-model","status":"publish","type":"post","link":"https:\/\/rudelabs.ai\/blogs\/car-price-prediction-machine-learning-model\/","title":{"rendered":"Car Price Prediction Machine Learning Model"},"content":{"rendered":"<h2>Introduction<\/h2>\n<p>A car price prediction machine learning model is a type of algorithm that uses historical data on car sales and features to predict the price of a car. The model is trained on a dataset of car information such as make, model, year, mileage, condition, and the corresponding sale price. Once trained, the model can be used to predict the sale price of a car based on its features. Common techniques for creating a car price prediction model include linear regression, decision trees, and random forests.<\/p>\n<iframe loading=\"lazy\"  id=\"_ytid_10991\"  width=\"1080\" height=\"607\"  data-origwidth=\"1080\" data-origheight=\"607\" src=\"https:\/\/www.youtube.com\/embed\/iEWwsvPsy5M?enablejsapi=1&autoplay=0&cc_load_policy=0&cc_lang_pref=&iv_load_policy=1&loop=0&rel=1&fs=1&playsinline=0&autohide=2&theme=dark&color=red&controls=1&\" class=\"__youtube_prefs__  no-lazyload\" title=\"YouTube player\"  allow=\"fullscreen; accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen data-no-lazy=\"1\" data-skipgform_ajax_framebjll=\"\"><\/iframe>\n<p>&nbsp;<\/p>\n<h2>Objectives<\/h2>\n<p>The objective behind building this car price prediction machine learning model is<\/p>\n<ul>\n<li>To predict the price of a car so that we can get our car according to our own utility and demand balanced according to our price range.<\/li>\n<li>To help businesses in the automobile industry to set standards to meet the requirements of the users and can also grow their businesses accordingly.<\/li>\n<li>To use historical data to train the model and make predictions on new, unseen data.<\/li>\n<\/ul>\n<h2>Requirements<\/h2>\n<p>To build a car price prediction model using Python, you will need the following:<\/p>\n<ul>\n<li><strong>A dataset of car information:<\/strong> This dataset should include features such as make, model, year, mileage, and condition, as well as the corresponding sale price.<\/li>\n<li><strong>Python programming language:<\/strong> You must install Python on your computer to build the model.<\/li>\n<li><strong>Required Libraries:<\/strong> You will need to install libraries such as <strong><em>numpy, pandas, scikit-learn, and matplotlib.<\/em><\/strong> These libraries are used in Python for data manipulation, visualization, and machine learning.<\/li>\n<li><strong>Jupyter Notebook\/ IDE:<\/strong> You will need a development environment such as Jupyter Notebook or an IDE to write and run the code for the model.<\/li>\n<li>Understanding of Machine Learning concepts and Python programming.<\/li>\n<\/ul>\n<h2>Source Code<\/h2>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import seaborn as sns\r\n\r\nimport pandas as pd\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nimport numpy as np\r\n\r\n%matplotlib inline\r\n\r\ndf=pd.read_csv('car data.csv\u2019)\r\n\r\ndf.shape\r\n\r\nprint(df['Seller_Type'].unique())\r\n\r\nprint(df['Fuel_Type'].unique())\r\n\r\nprint(df['Transmission'].unique())\r\n\r\nprint(df['Owner'].unique())\r\n\r\n##check missing values\r\n\r\ndf.isnull().sum()\r\n\r\ndf.describe()\r\n\r\nfinal_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]\r\n\r\nfinal_dataset.head()\r\n\r\nfinal_dataset['Current Year']=2022\r\n\r\nfinal_dataset.head()\r\n\r\nfinal_dataset['no_year']=final_dataset['Current Year']- final_dataset['Year']\r\n\r\nfinal_dataset.head()\r\n\r\nfinal_dataset.drop(['Year'],axis=1,inplace=True)\r\n\r\nfinal_dataset.head()\r\n\r\nfinal_dataset=final_dataset.drop(['Current Year'],axis=1)\r\n\r\nfinal_dataset.head()\r\n\r\nfinal_dataset.corr()\r\n\r\nsns.pairplot(final_dataset)\r\n\r\nimport seaborn as sns\r\n\r\n#get correlations of each features in dataset\r\n\r\ncorrmat = df.corr()\r\n\r\ntop_corr_features = corrmat.index\r\n\r\nplt.figure(figsize=(20,20))\r\n\r\n#plot heat map\r\n\r\ng=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")\r\n\r\nfinal_dataset.head()\r\n\r\nX=final_dataset.iloc[:,1:] # independent feature\r\n\r\ny=final_dataset.iloc[:,0] # dependent feature (selling price)\r\n\r\nX.head()\r\n\r\ny.head()\r\n\r\n# feature importance\r\n\r\nfrom sklearn.ensemble import ExtraTreesRegressor\r\n\r\nmodel = ExtraTreesRegressor()\r\n\r\nmodel.fit(X,y)\r\n\r\nprint(model.feature_importances_) # according to the value this tells us the importance of features\r\n\r\n#plot graph to better visualize feature importances\r\n\r\nfeat_importances = pd.Series(model.feature_importances_, index=X.columns)\r\n\r\nfeat_importances.nlargest(5).plot(kind='barh')\r\n\r\nplt.show()\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\r\n\r\nX_train.shape\r\n\r\nfrom sklearn.ensemble import RandomForestRegressor\r\n\r\nregressor=RandomForestRegressor()\r\n\r\n# hyperparameters for decision trees\r\n\r\nn_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\r\n\r\nprint(n_estimators)\r\n\r\n# Number of trees in random forest\r\n\r\nn_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\r\n\r\n# Number of features to consider at every split\r\n\r\nmax_features = ['auto', 'sqrt']\r\n\r\n# Max number of levels in the tree\r\n\r\nmax_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\r\n\r\n# max_depth.append(None)\r\n\r\n# Min number of samples that are required to split a node\r\n\r\nmin_samples_split = [2, 5, 10, 15, 100]\r\n\r\n# Min number of samples that are required at each leaf node\r\n\r\nmin_samples_leaf = [1, 2, 5, 10]\r\n\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\n\r\n#Randomized Search CV\r\n\r\n# Create the random grid\r\n\r\nrandom_grid = {'n_estimators': n_estimators,\r\n\r\n'max_features': max_features,\r\n\r\n'max_depth': max_depth,\r\n\r\n'min_samples_split': min_samples_split,\r\n\r\n'min_samples_leaf': min_samples_leaf}\r\n\r\nprint(random_grid)\r\n\r\n# Using the random grid to search best hyper-parameters\r\n\r\n# First create the base model to tune\r\n\r\nrf = RandomForestRegressor()\r\n\r\n# Random search of parameters by using 3 fold cross validation\r\n\r\n# search across 100 different combinations\r\n\r\nrf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42,n_jobs=1)\r\n\r\nrf_random.fit(X_train,y_train)\r\n\r\nrf_random.best_params_\r\n\r\nrf_random.best_score_\r\n\r\npredictions=rf_random.predict(X_test)\r\n\r\nfrom sklearn import metrics\r\n\r\nprint('MAE:', metrics.mean_absolute_error(y_test, predictions))\r\n\r\nprint('MSE:', metrics.mean_squared_error(y_test, predictions))\r\n\r\nprint('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))\r\n\r\nimport pickle\r\n\r\n# open a file, where you want to store the data\r\n\r\nfile = open('random_forest_regression_model.pkl', 'wb')\r\n\r\n# dump information to that file\r\n\r\npickle.dump(rf_random, file)<\/pre>\n<h2>Explanation of the Code<\/h2>\n<p>1. Initially, we imported the dataset and all the necessary libraries that were needed to build our model.<\/p>\n<p>2. Then, we checked for the null values in our dataset, and if present, we removed them accordingly.<\/p>\n<p>3. According to our features, we have cleaned our dataset and dropped some of the columns which are not useful in our model-building process.<\/p>\n<p>4. Then, in the next section, we started our train test split phase and trained the model with <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html\">Random Forest Classifier<\/a>, and then with the Randomized Search CV, we selected the best number of attributes for our model building.<\/p>\n<p>5. We have created some plots and visualizations to get insights from our dataset more concisely.<\/p>\n<p>6. Then, accordingly, we predicted the values after the training phase was done.<\/p>\n<h2>Output<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-17855 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2023\/01\/Car-Price-Prediction-Machine-Learning-Model.webp\" alt=\"Car Price Prediction Machine Learning Model\" width=\"940\" height=\"831\" \/><\/p>\n<h2>Conclusion<\/h2>\n<p>Hence we have successfully built the car price prediction machine learning model. This model will predict the price of a car based on given features in our dataset, which will help individuals to select the best-suited car according to their own utility and demand. Hence this model can also help businesses grow and increase revenues.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This machine learning model can be used to predict the sale price of a car based on its features.<\/p>\n","protected":false},"author":1,"featured_media":10372,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-10371","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-coding-projects"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Car Price Prediction Machine Learning Model - RUDE LABS<\/title>\n<meta name=\"description\" content=\"A car price prediction machine learning model is a type of algorithm that uses historical data on car sales and features to predict the price of a car.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rudelabs.ai\/blogs\/car-price-prediction-machine-learning-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Car Price Prediction Machine Learning Model - 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