karammajdi7 / house_price_prediction

House_Price_Prediction

This project was created using Machine Learning with Python, the project was made by using the Boston house price prediction dataset, which is a dataset that comes from UCL machine learning repository, and this data was collected in 1978, the data has 506 entries that represents aggregate information about 14 features of homes located in boston.

In this particular project I used XGBoost Regression, and it's regression beacuse it's about predicting a quantity, and firstly in the project I imported several libraries such as pandas, matplotlib, sklearn, numpy and seaborn, then I loaded the boston dataset using the sklearn datasets, then I created a dataframe using pandas then I included the target which is the price value of this dataframe, and the I printed the shape of the dataframe which is (506, 14), the I checked if there was any missing values in the data, and then I checked the statistical measures which was the mean value and others, then I checked for the correlation between the various features in the dataframe using a heatmap, and then splitted the dataset into data and label, and then splitted the data into training and test data with the help of the train test split function, and I stated the test size to 20 percent, and then trained the XGBoost regression model usnig the XGBRegressor function, and the train the model with fit function with the train data, I have evaluted the data for both training and test data, and I checked the error values which was quite good, and at last pltted the actual price and the predicted price in a graph using the matplotlib library, and the results were very close to each other.