This is a repository describing my new learnings from datasets and the techniques I used on them

• #Day0: -> Creating projects timeline

      -> Setting up Ubuntu and CUDA (2 years ago it was stll a hassle & today it still is)
      
      -> Installing packages and libraries
      
      -> Setting up my music playlist
      
      -> Uninstall Instagram (My hatred for time-wasting social media knows no bounds)
  

• Data preprocessing - 80% time of a Data scientist goes, or at least should go doing this (Pareto principle everywhere)

      -> Importing dataset
      
      -> Handling missing data
      
      -> Encoding categorical data
      
      -> Splitting into training set and test set
      
      -> Feature scaling
  

• Linear Regression

      -> Predicted salaries of employees based on their experience
  

• Multivariable Linear Regression

      -> Encoding categorical data
      
      -> Label Encoder
      
      -> One hot Encoder
      
      -> Feature scaling
      
      -> Predicted profit based on multiple independent features.
  

• Boston house pricing

      -> used DESCR to describe the data
      
      -> Mean, median, Standard deviation analysis
      
      -> Plotted real values v/s predicted values to get an idea of the prediction
  

• Polynomial regression

      -> Learned about Polynomial regression and how it's degree affects the curve
  

• Principal Component Analysis

      -> Performed PCA on Cancer dataset
  

• Data Analysis

     -> Data Analysis of Habermann survival dataset (Breast cancer)
     
     -> PDF and CDF
     
     -> Histograms
     
     -> Box plots
     
     -> Violin plots
     
     -> Pair plots
     
     -> Heatmaps