/AI-Trainee-Week-3

work done on the third week of AI Traineeship

Primary LanguageJupyter Notebook

AI Trainee - Week_3

Linear Regression Exploration

  1. Simple linear regression
  2. Loss function: MSE, MAE
  3. Manual backpropagation
  4. Built and Trained the model

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Assignment

Dataset: Housing.csv

Objective

To predict the price of houses based on the given features.

Work Flow:

  1. Performed Exploratory Data Analysis
  2. Handled the skewness and outliers from the features: price and area.
  3. Used Log transformation to handle skewness.
  4. Analyzed categorical features and perform nominal encoding on them except for furnishingstatus.
  5. For furnishingstatus, checked out ordinal as well as one-hot encoding to see what gives better result.
  6. Analyzed Correlation plots and dropped some features having low correlation with the target. -- hotwaterheating.
  7. Checked if independent features are also corelated or not. Found some. But dropping them didn't improve the overall result.\
  8. Split the dataset into train and test set and also scale the data.
  9. Built Linear Regression Model.
  10. Used MSE,MAE and Log(cosh(x)) as the cost function.
  11. Trained the model on each one of them.
  12. Used R2 score as the evaluation metric.

Output:

Training Set : 0.6921 Test Set : 0.6837