README.md

PredictHousePriceML

In this notebook:

1. Load a dataset and perform basic data exploration using pandas.
2. Preprocess the data for linear regression.
3. Compute the cost and perform gradient descent in pure numpy in vectorized form.
4. Fit a linear regression model using a single feature.
5. Visualize your results using matplotlib.
6. Perform multivariate linear regression.
7. Pick the best three features in the dataset.

DB:

1. containing housing prices in King County, USA.
2. contains 5,000 observations with 18 features and a single target value - the house price.

We will predict a house price based on previous observations using the following methods:

Linear Regression

Gradient Descent

Find best features for regression

Backward Feature Selection