Week 1 Solutions - Mansi Pandey

[mansipandey18]

1. I learned how to plot lines using Python and understand how slope (m) and intercept (b) shape a line.
2. I understood that slope controls the tilt of the line and intercept moves the line up or down.
3. I learned how to calculate predictions using the formula y = mx + b.
4. I learned what error means in machine learning — the difference between actual value and predicted value.
5. I also learned how to save Jupyter notebooks as .ipynb in VS Code.

[subhasmitasahoo]

Added some comments on code.

[subhasmitasahoo]

3 & 4 are missing

[subhasmitasahoo]

missing code

[subhasmitasahoo]

65 is wrong

[subhasmitasahoo]

Can you save output of each excercise cell, so that its easy for me to review. I see errors for many cells output.

[subhasmitasahoo]

similarly save output for each cell in bonus challenge as well.

[mansipandey18]

What I Learned:

1. I learned how Linear Regression Works
2. Calculating Slope & Intercept Manually
3. I learned how to use this for estimating future values.
4. Residuals = difference between actual and predicted values. Helps check whether the line fits well or poorly.
5. Learned to calculate how well the model explains the data. R^2 close to 1 means excellent fit. R^2 near 0 means poor fit.
6. Plotting Regression Line
7. Multiple Linear Regression

[mansipandey18]

✅ Completed the house price prediction exercise using linear regression! I learned how to:

1. Analyze data and find patterns (size vs price)
2. Build a model step by step (calculate slope and intercept)
3. Make predictions for new houses using Price = m × Size + b
4. Evaluate the model with errors and R^2 score