Predicting-Housing-Prices-using-regression-models | Here the main objective

Predicting-Housing-Prices-using-regression-models is a Jupyter Notebook library. Predicting-Housing-Prices-using-regression-models has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Analysed a dataset from Kaggle and performed an exploratory data analysis. Here the main objective was to predict the price of a house in Bengaluru based on its location, area type, total sqft, no of bedrooms, and no of bathrooms. The above objective was achieved by data cleaning, errors in the dataset were solved by either generalizing them or not including them in the dataset if having the least importance in our model building, outlier detections, and removal, use of K- fold cross-validation so as to protect the model from overfitting, checking the accuracy of the model, etc. Libraries used were NumPy, Pandas, Matplotlib, Seaborn, and Sklearn.