Sourav Rawat

Looking for Opportunities to gradually carve a niche for myself in the Data Science and Analytics field.

* ITP/NPV, Skills and Tools: Python, Numpy, Pandas

Perform the data analysis to get the different insights using the data collected from the IPL matches. The dataset contains 2 files ‘deliveries.csv' and ‘matches.csv'. matches.csv contains details related to the match such as location, contesting teams, umpires, results, etc. deliveries.csv contains the information about each delivery bowled in all the IPL matches line runs scored, batsman, bowler, non-striker, etc.,

*Online Shopping Process Analytics, Skills and Tools: MySQL queries

To learn and understand the purchasing behavior of online customers, the data is confined to ‘Sales and Delivery' and is provided for the period of last decade. This will be useful in · Analyzing seasonality & business patterns, Product analysis, User analysis, order placed, orders delivered.

*Descriptive Statistics on real world problem,

Skills and Tools: Descriptive and inferential statistics, Hypothesis testing, confidence interval, Distribution.

The first problem statement focuses on exploratory data analysis on a la liga cup (a football tournament). Learners need to analyze the data to get useful insights on top teams, winning distribution etc. The second problem statement helps in understanding and applying confidence intervals to a healthcare related problem. The third problem statement will make learners apply statistical techniques like design experiment and hypothesis testing on designing golf data.

*Sports Analytics, Skills and Tools: ITP, NPV and EDA

A new football club named ‘Brussels United FC' has just been inaugurated & the team is looking to hire players for their roster. Management wants to make such decisions using data based approach. Player data for all teams has been acquired from FIFA. The data contains details for over 18,000 players playing in various football clubs in Europe. It contains information on age, skill rating, wages and player value, etc. in the files named fifa.csv which is data file and fifa_variable_information.cs

*Machine Learning model building for continuous variable prediction,

Skills and Tools: Machine Learning, Supervised Learning, Linear Regression.

Build a Linear Machine Learning model to understand the relationship between the population of US cities in the year 1920 and 1930. Evaluate the model performance with appropriate measures. Perform all the required graphical and quantitative exploratory data analysis prior to model building. The dataset has 49 rows and 2 columns. The measurements are the population (in 1000's) of 49 U.S. cities in 1920 and 1930. The 49 cities are a random sample taken from the 196 largest cities in U.S titative exploratory data analysis prior to model building. and 1930. The 49 cities are a random sample taken from the 196 largest cities in U.S

• Achieved rank 179 by completing wilson analytic hackathon with 99.40% accuracy.

• Python Programming
• SQL
• Numpy Pandas & Visualization
• Statistic For Data Science
• Exploratory Data Analysis
• Linear Regression
• Supervised Classification
• Unsupervised Classification