Project to classify elections data using supervised classification
1.1)Read the dataset. Describe the data briefly. Interpret the inferences for each. Initial steps like head() .info(), Data Types, etc. Null value check, Summary stats, Skewness must be discussed.
1.2)Perform EDA (Check the null values, Data types, shape, Univariate, bivariate analysis). Also check for outliers Interpret the inferences for each (3 pts) Distribution plots(histogram) or similar plots for the continuous columns. Box plots, Correlation plots. Appropriate plots for categorical variables. Inferences on each plot. Outliers proportion should be discussed, and inferences from above used plots should be there. There is no restriction on how the learner wishes to implement this but the code should be able to represent the correct output and inferences should be logical and correct.
1.3)Encode the data (having string values) for Modelling. Is Scaling necessary here or not, Data Split: Split the data into train and test (70:30) The learner is expected to check and comment about the difference in scale of different features on the bases of appropriate measure for example std dev, variance, etc. Should justify whether there is a necessity for scaling. Object data should be converted into categorical/numerical data to fit in the models Data split, ratio defined for the split, train-test split should be discussed.
1.4)Apply Decision Tree and Random Forest Model. Interpret the inferences of each model Successful implementation of each model. Logical reason behind the selection of different values for the parameters involved in each model. Calculate Train and Test Accuracies for each model. Comment on the validation of models (over fitting or under fitting).
1.5 Performance Metrics: Check the performance of Predictions on Train and Test sets using Accuracy, Confusion Matrix, Plot ROC curve and get ROC_AUC score for each model, classification report Final Model - Compare and comment on all models on the basis of the performance metrics in a structured tabular manner.