Real-Estate-Project-Machine-Learning

1. Analyzing the problem from Collecting Data.

2. Importing it to a Jupyter Notebook.

3. Looking for Promising Attributes.

4. Finding out Correlations.

5. Plotting graphs, Creating a pipeline.

6. Dealing with Missing Values (Replacing With Mean or Median).

7. At the end we present the problem to the real estates company who will use the model for predicting house prices given a set of features (Final Model Price Predictor.ipynb).

8. Using Concepts like Cross Validation, Train-Test Splitting, Stratified Shuffle Split.

9. Trying 3 Differenet Algorithms

   1. Decisson Tree

   2. LinearRegression

   3. Random Foret Regressor

10. Feature Scaling

    1. Min-Max scaling (Normalization)

       (value - min)/(max - min)

       from sklearn.preprocessing import MinMaxScaler

    2. Standardization (value - mean)/standard deviation

       scales them such that the distribution centered around 0, with a standard deviation of 1.