🏡Predict Housing Prices💲 by Multivariable Regression using Pandas, Numpy, Matplotlib, Seaborn, Plotly, & Sci-kit Learn of Python
🌟Welcome to Boston Massachusetts in the 1970s! Imagine you're working for a real estate development company.
🌟 We have built a model that can provide a price estimate based on a home's characteristics like:
❓The number of rooms
❓The distance to employment centres
❓How rich or poor the area is
❓How many students there are per teacher in local schools etc
💪All of this is achieved using Mulivariable Regression.
👉We first use Pandas, Numpy, Matplotlib & Seaborn to analyse the Housing data provided.
👉Next, we use Plotly to create beautiful & interactive charts which can be used to design and think of possisible model characteristics.
👉Then, we set up the model using Sci-kit Learn library. We use the 80-20 rule to design and build our model where, 80% data is used as Training data & remaining 20% data is used for Testing the predictions made by the developed model.
👉Again we use Numpy, Matplotlib, Plotly & Seaborn to visualize and compare the predicted values to the original expected values. This makes it easy to verify our models efficiency and performance.
🌟Thus Finally the Model is Tried and Tested and is ready to Make Proper Predictions of the House Prices with the appropriate data provided by the user.
👉From this we come to know:
✅How to quickly spot relationships in a dataset using Seaborn's .pairplot().
✅How to split the data into a training and testing dataset to better evaluate a model's performance.
✅How to run a multivariable regression.
✅How to evaluate that regression-based on the sign of its coefficients.
✅How to analyse and look for patterns in a model's residuals.
✅How to improve a regression model using (a log) data transformation.
✅How to specify your own values for various features and use your model to make a prediction.
🌟The Sample of the Notebook used for carrying out all of the above mentioned operations is given below
👆Sample of Multivariable_Regression_and_Valuation_Model👆