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Understanding the problem statement

Importing the dataset

Initializing necessary libraries and understanding its use

Performing basic EDA on the dataset

Visualizing outliers using boxplot and whiskers plot

Fixing the outliers using IQR method

Creating new features form existing features/Feature engineering

Preparing the dataset for fitting it into a model

Applying Logistics Regression

Understanding "Sensitivity" and "Specificity"

ROC and AUC curve as evaluation metrics

Sampling technique called as Bagging and boosting technique

Applying Gradient Boosting and Random Forest

Making final predictions

Learn to classify the sentiment of sentences from the Rotten Tomatoes dataset. You will be asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive.

In this project, we are going to work on Deep Learning using H2O to predict Census income.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Problem Statement

03m

Classification vs Prediction

03m

Import Data Set

02m

Data Set Exploration

20m

Data Validation - Missing Values

38m

Outliers Overview

05m

Boxplot to Identify Outliers

01m

Quantile Method

09m

Replace Outliers

02m

What is Linear Regression?

15m

What is Logistic Regression?

06m

Convert Data Type to factor

13m

Build a Regression Model

11m

Model Summary

02m

Prediction

01m

Calculate Event Rate

07m

ROC Curve method

13m

Decision Trees

14m

Prediction using Trees

12m

Sampling Techniques

07m