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nirdesh17 / repository
This project demonstrates the application of machine learning techniques to predict house prices based on various features. By analyzing the dataset, preprocessing the data, and selecting an appropriate model, we were able to achieve a high level of accuracy in predicting house prices. The trained model can be further refined and deployed.
This project involves building a machine learning model to predict house prices based on various features. The dataset used for this project is from the Kaggle competition "House Prices - Advanced Regression Techniques". The goal is to develop a model that accurately predicts house prices given a set of input features.
house_price_prediction.ipynb: Jupyter Notebook containing the code for data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and prediction.submission.csv: CSV file containing the predicted house prices for the test dataset.gbr.pkl: Pickle file containing the trained GradientBoostingRegressor model.submission.csv) for submission.submission.csv file contains the predicted house prices for the test dataset.gbr.pkl) is stored as a pickle file for future use or deployment.For any further inquiries or improvements, feel free to reach out.
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AkshaySatasiya /
This project showcases the integration of advanced AI models for query and answer generation in a chatbot, complemented by a Streamlit application for an interactive user interface. It includes multiple Jupyter notebooks, each demonstrating a different aspect of chatbot functionality using OpenAI and GEMINI models.