[SOLVED] Csci 183 homework 2

Regression for House Price Prediction
What do you need to do?
1. Load the dataset into a pandas dataframe and find the data types for each
column in the dataset.
2. Find the names of the columns of this dataframe.
3. Find how many numerical features exist in the dataset.
4. Find the correlation matrix for this dataset. Report which features tend to have a
high correlation with the target variable. (You can use the corr() function). Refer
to supplementary slide ‘Correlation’.
5. Create and compile as many graphs (feature vs target variable) as you can using
the matplotlib library [https://matplotlib.org/gallery/index.html] for the given
dataset. Select only numerical features.
6. Based on the graphs in step 5, identify features that have a linear relationship
with the target variable.
7. Selecting different features from step 6, implement a linear regression algorithm
and find the slope, the intercept and the error of the regression model.
8. Display the line of best fit from step 7.
9. Some options you can consider:
a. linregress() from scipy.stats
b. LinearRegression() from sklearn
c. Manually code the gradient descent algorithm
10.Create a table similar to one given below for all the features selected in step 7.
11. Also attach images of the graphs to your report.
Observation Table:
Feature Slope Intercept
:
:
Submit your code as an .ipynb file and a document reporting your findings. You could
also show them as output of your code.