Used Car Price Prediction with Machine Learning

Excited to share my recent project experience! 🚗💡 I had the opportunity to work on a fascinating machine learning project where I predicted the prices of used cars using a dataset from PakWheels. I took on the challenge of meticulously cleaning and preprocessing the data, diving deep into factors like brand, mileage, condition, and manufacturing year that significantly impact car prices. Through rigorous analysis and model development, I evaluated different machine learning algorithms to determine which one provided the most accurate predictions. This project not only sharpened my data handling and predictive modeling skills but also highlighted the transformative power of data analytics in making informed decisions. Looking forward to applying these insights in future endeavors! #MachineLearning #DataScience #PredictiveAnalytics #DataDrivenDecisionMaking

21 | undergraduate | BBA | Marketing | Human Resource | The citizen Foundation |

2mo

Great work sir!

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Harshita Chourasiya Attended Jaypee University of Engineering and Technology 3mo Edited 🚗 Car Price Prediction with Machine Learning 🔍 I recently worked on an exciting project where I built a Machine Learning model to predict car prices based on various factors like brand, mileage, horsepower, and features. This project helped me explore data preprocessing, feature engineering, and model evaluation techniques in depth. 📊 Data Analysis and Visualization 🤖 Model Training and Optimization #MachineLearning #DataScience #CarPricePrediction #AIProjects #LearningJourney Oasis Infobyte #oasisinfobyte

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HASSAN MUHAMMED NLP| Machine Learning | Deep Learning |Data Scientis 11mo Hello LinkedIn community Would you like to know how machine learning techniques can help accurately predict car prices? Here's a deep dive into our new project! 🔍 Idea: In this project, we analyze car market data to understand the factors that influence car price determination, and then use machine learning algorithms to predict car prices based on these factors. 🔧 Tools and techniques used: LinearRegression RandomForest DecisionTree XGBoost GradientBoosting SVR Streamlit (for user interface) 📊 Analysis process: Data collection: We collect various data about the technical and economic features of cars, such as model, brand, liter capacity, year, price, etc. Data Cleaning: We analyze the data and clean it of any missing or inaccurate data. Model Training: We use clean data to train models using the above algorithms. Performance Evaluation: We evaluate the performance of each model and choose the best model based on different performance metrics. 📈 Results: We get a sophisticated model that can predict car prices with high accuracy, enabling buyers and sellers to make better decisions in the car market. #Machin_Learning #Car_Price_Prediction #DataScience #Predicting #Machine_Learning #Full_Data_Science_Project #Streamlit

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Jawad Waseem Business Analytics student | Content Writer 7mo 🚗 Just completed a Car Price Prediction analysis at CodeAlpha I utilized machine learning algorithms to predict car prices based on features like age, mileage, make, and fuel type. This project involved data cleaning, feature engineering, and model selection to ensure accurate results. By applying regression techniques, I was able to determine key factors that influence car prices and improve model performance. The insights from this analysis can help dealerships, buyers, and sellers make more informed decisions. Here's the GitHub link: https://lnkd.in/dVGFxB6R #DataScience #MachineLearning #CarPricePrediction #DataAnalytics #PredictiveModeling #BusinessAnalytics

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Satyam Gupta Data science || Data analyst ||Machine Learning || Deep Learning || NLP || Langchain || LLM || SQL || Power BI || Advance Excel 7mo Exciting Project Alert! I'm thrilled to share my latest project on Car Price Prediction using Machine Learning! Objective: To build a model that accurately predicts car prices based on various features like make, model, year, mileage, and more. Approach: Employed a combination of data preprocessing, feature engineering, and machine learning algorithms (Linear Regression, ensemble, Random Forest) to build the model. Results: Achieved an accuracy of 90% in predicting car prices! Key Takeaways: - Data preprocessing is crucial for building a robust model. - Feature engineering can significantly improve model performance. - Ensemble methods can lead to better accuracy. If you're interested in learning more about this project or exploring opportunities in machine learning, feel free to connect and let's chat! #MachineLearning #CarPricePrediction #DataScience #ProjectSpotlight"

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DEVA VINOTH Founder at MakersVerse | Content Creator | AI & ML Engineer | Mobile App Developer 9mo 🚗 Excited to Announce My Latest Project: Car Price Prediction Model! 🚗 I am thrilled to share my latest machine learning project, where I developed a Car Price Prediction Model using the Quikr Car Dataset. This model leverages the power of Linear Regression to predict car prices based on various features such as make, model, year, engine fuel type, and more. 🌟 Key Highlights: Dataset: Quikr Car Dataset, featuring comprehensive car attributes. Model: Trained a robust Linear Regression model to provide accurate price predictions. Implementation: Utilized Jupyter Notebook for development and model training. 🔧 How to Use the Model: Clone the Repository: git clone Install Required Libraries: pip install -r requirements.txt Run the Jupyter Notebook: jupyter notebook Train the Model and Make Predictions: Open quikr_predictor.ipynb and execute the cells to get started! This project is a testament to the potential of machine learning in real-world applications. It can significantly aid in understanding and predicting car prices, helping buyers and sellers make informed decisions. 🌐 Connect with Me: MakersVerse(https://lnkd.in/g_CptUTA) LinkedIn(https://lnkd.in/gJqeUhsm) GitHub(https://lnkd.in/gMCqp_KR) Portfolio(https://lnkd.in/gS6QZY27) I'd love to hear your thoughts and feedback on this project. Feel free to connect and share your insights! Have a great day! 😄 #MachineLearning #DataScience #ArtificialIntelligence #CarPricePrediction #LinearRegression #QuikrDataset #MLProjects 4 Like Comment To view or add a comment, sign in

Abhiram K Data Science and Machine learning || Deep Learning || NLP || Excel || PowerBI || MySQL || Python || Master of Science in physics 2mo Just wrapped up an exciting project on Car Price Prediction for the American market! 🚗📈 I started by diving deep into the dataset, applying rigorous preprocessing techniques to ensure robust model performance. Key steps included handling skewness and selecting the right scaling methods to normalize the data. Once the data was prepped, I moved on to model training and selection, where I compared several algorithms before fine-tuning the best one. I ultimately implemented the GradientBoostingRegressor—a powerful ensemble method that combines multiple decision trees to boost prediction accuracy. After thorough hyperparameter tuning, the model not only showed improved performance, but also achieved the following evaluation metrics: * MAE (Mean Absolute Error): 0.0893 * MSE (Mean Squared Error): 0.0145 * RMSE (Root Mean Squared Error): 0.1204 * R² (Coefficient of Determination): 0.9071 These metrics indicate that the model now explains 90.71% of the variance in car prices, a significant improvement over previous iterations. This project has been a fantastic learning experience in data preprocessing, model optimization, and the application of ensemble methods for regression tasks. Looking forward to leveraging these insights in future projects and further exploring the intersection of data science and the automotive industry! Checkout my repository : https://lnkd.in/g8FauHzp #CarPricePrediction #GradientBoosting #Regression #DataScience #MachineLearning #ModelTuning #HyperparameterTuning #DataPreprocessing #FeatureEngineering #PredictiveModeling #DataAnalytics #AutomotiveIndustry #AutoTech #BigData #DataMining #DataVisualization #PythonProgramming #MLAlgorithms #EnsembleLearning #AI #ArtificialIntelligence #TechInnovation #BusinessIntelligence #DigitalTransformation #SmartCars #Innovation #TechTrends #StatisticalModeling #DataDriven #CodingLife 24 Like Comment To view or add a comment, sign in

Harshini Manikandan -- 3mo Edited 🌟 Successfully Completed a Project- Car Price Prediction with Machine Learning (Task3)🌟 Excited to share that I successfully completed a Car Price Prediction project using Machine Learning! In this project, I built a predictive model to estimate car prices based on factors like brand reputation, features, horsepower, mileage, and more. This involved: ○Data preprocessing and feature engineering. ○Training machine learning models to make accurate predictions. ○Evaluating and fine-tuning the model for optimal performance. Repository link: https://lnkd.in/g8GuBNaX This project was a great learning experience, enhancing my skills in data analysis, feature selection, and model building. Grateful for the opportunity to work on such an impactful problem!

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Priyanka Todkar B.Tech (CS) | Data Science | Python Developer | AI and ML | NLP | Student at Sanjivani College of Engineering Kopargaon 10mo Edited 🎉 Excited to Share My Latest Project!*🚀 Just completed a project on Car Price Prediction as part of my data science journey! 🔎Project Overview: Objective: Predict car prices based on various features using machine learning techniques. Data Preparation: Imported necessary libraries, loaded and inspected the car dataset. Data Exploration: Visualized data distributions and relationships between features. Data Preprocessing: Handled missing values, encoded categorical variables, and scaled features. Model Training: Trained models such as Linear Regression, Decision Trees, and focused on the Random Forest Regressor. Evaluation: Assessed model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. Final Model: Optimized the Random Forest Regressor with the best parameters and made predictions. Results: Visualized and compared actual vs. predicted car prices to evaluate model accuracy. This project allowed me to apply essential machine learning techniques, deepen my understanding of regression algorithms, and showcase skills in data preprocessing and model training. Check out the detailed analysis and code here: 👇 Code Link : https://lnkd.in/d_DEmM2A #DataScience #MachineLearning #CarPricePrediction

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Rohit Kumar Python & R Developer || Data Analytics || Fronted Developer || Machine Learning || Power Bi || SQL || C & C++ || Core Java || Advance MS EXCEL || Pursuing BTECH in Data Science. 6mo 🎉 Presenting my latest work: 🎉 Car Price Predictor! Problem statement. This dataset comprises used cars sold in India as well as important features of these cars. Here, user can predict the price of the car based on input features. Prediction results can be used to give new seller the price suggestion based on market condition. Steps: Data Collection & Loading: Import libraries (pandas, numpy, sqlalchemy) and load the dataset. Establish a connection to SQL if storing or retrieving data from a database. Data Preprocessing: Handle missing values, remove duplicates, and clean irrelevant data. Encode categorical features like car brand, fuel type using pd.get_dummies(). Exploratory Data Analysis (EDA): Use matplotlib to visualize relationships between features (e.g., mileage vs. price, year vs. price). Calculate correlations to determine which features affect price the most. Model Building: Split data into training and test sets using train_test_split. Apply machine learning algorithms (e.g., Linear Regression, Random Forest) to predict prices. Model Evaluation: Use metrics like Mean Squared Error (MSE), R-squared, and MAE to evaluate model performance. GUI with Tkinter: Build a graphical interface using Tkinter to allow users to input car details and get predicted price outputs in real-time. Deployment: Package the model and GUI for easy user interaction GitHub Project:-https://lnkd.in/gMaz-SYM #DataScience #MachineLearning #Project

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Achyuth Govindula Data Analyst | Digital Marketing | Graphic Design 6mo I have successfully completed Task-3 on Car Price Prediction using Machine Learning as part of the Oasis Infobyte program! Task-3 : Car Price Prediction using Machine Learning Task Description : The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. Car price prediction is one of the major research areas in machine learning. So if you want to learn how to train a car price prediction model then this project is for you. Code Description : The code implements a Random Forest Regressor to predict car prices by first preparing the dataset, which involves separating the target variable and converting categorical features into dummy variables. It then splits the data into training and testing sets, trains the model on the training data, and makes predictions on the test set. Model performance is assessed using metrics like Mean Squared Error (MSE) and R² score. Additionally, feature importances are ranked to highlight the factors that have the most significant impact on the car price predictions. Click link below to download the code : https://lnkd.in/gZapq3bE #MachineLearning #DataScience #OasisInfobyte

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