Data-Analysis

Details various python libraries for performing exploratory data analysis. The repository will help any aspiring data analyst, to understand Python libraries like Pandas, Numpy, Seaborn, Matplotlib, ScikitLearn. The repository contains multiple jupyter notebook , explaining various python libraries used in data analysis.

Dataset :

https://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.asp The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. Here we will focus on the Canadian Immigration data.

The first notebook (introduction.ipynb) covers :

• Introduction to data that will be used throughout this repository,
• Importing and Exporting Data in Python,
• Getting Started Analyzing Data in Python, Python Packages for Data Analysis.

The second notebook(Data-Wrangling.ipynb) covers :

• Identify and Handle Missing Values,
• Data Formatting,
• Data Normalization,
• Binning,
• Indicator variables.

The third notebook (exploratory-data-analysis.ipynb) covers :

• Descriptive Statistics,
• Basic of Grouping,
• ANOVA,
• Correlation.

The fourth notebook (model-development.ipynb) covers :

• Simple and Multiple Linear Regression,
• Model Evaluation Using Visualization,
• Polynomial Regression and Pipelines,
• R-squared and MSE for In-Sample Evaluation,
• Prediction and Decision Making.

The fifth notebook (model-evaluation-and-refinement.ipynb) covers:

• Model Evaluation,
• Over-fitting, Under-fitting and Model Selection,
• Ridge Regression,
• Grid Search,
• Model Refinement.