Project 1: (Linear Regression and Logistic Regression)

Project 1: (Linear Regression and Logistic Regression)

Predicting House Prices Using Simple Linear Regression

Project Description:

In this project, we will build a simple linear regression model to predict the prices of houses based on their size. We will use a dataset containing information about the prices and sizes of houses in a particular area to train and test the model.

Project Tasks:

1. Load the dataset: Load the house prices dataset and examine its structure.
2. Visualize the data: Use data visualization techniques to explore the relationship between the size of the house and its price.
3. Prepare the data: Split the dataset into training and testing sets. Scale the data using feature scaling to improve the performance of the model.
4. Train the model: Build a simple linear regression model using the training data.
5. Evaluate the model: Use the testing data to evaluate the performance of the model. Calculate the mean squared error and R-squared value to measure the accuracy of the model.
6. Visualize the results: Visualize the results of the model by plotting the predicted values against the actual values.
7. Predict new values: Use the trained model to predict the prices of new houses based on their size.

Tools:

– Python programming language

– Numpy and Pandas libraries for data manipulation

– Matplotlib and Seaborn libraries for data visualization

– Scikit-learn library for machine learning

– Jupyter Notebook for coding and presentation

Expected Outcome:

At the end of this project, we will have a simple linear regression model that can predict the prices of houses based on their size. We will also gain a deeper understanding of the concepts of linear regression, data preprocessing, model training, and model evaluation.