Post Graduate Diploma in Data Science | B.Tech in CSE | Skills : Python, Machine Learning, Data cleaning and processing , Statistics , Data Visualisation etc.
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Disaster_Response_Pipeline_Project
May 30, 2019 – June 1, 2019
Goal is to categorize disaster events so that you can send the messages to an appropriate disaster relief agency.
View ProjectHR-Analytics
May 16, 2019 – May 29, 2019
The company wants to understand what factors contributed most to employee turnover and to create a model that can predict if a certain employee will leave the company or not. The goal is to create or improve different retention strategies on targeted employees. Overall, the implementation of this model will allow management to create better decision-making actions.
View Projectcustomer_churn_analysis
February 28, 2019 – February 28, 2019
Goal is to predict the churn of customers for a telecom comapany
View ProjectCar_Price_Prediction-Multiple_Linear_Regression
July 29, 2018 – July 29, 2018
A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of a car depends. Specifically, they want to understand the factors affecting the pricing of cars in the American marketing, since those may be very different from the Chinese market. Essentially, the company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the American market. Goal of Analysis and prection : You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent var
View ProjectHousing_Price_Prediction_Multiple_Linear_Regression
July 23, 2018 – July 23, 2018
House price prediction using multiple linear regression and assessing model fit using VIF,P-value and Rsuared method
View ProjectRisk-Analytics-Loan-Approval-EDA
July 10, 2018 – July 10, 2018
a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company
View ProjectHouse-Prices-Prediction
July 10, 2018 – July 10, 2018
Use of Advance ML techniques : Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting Acknowledgments The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.
View ProjectTitanic-Survival-Case-Study
July 10, 2018 – July 10, 2018
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong interest in data science and machine learning, aligning with a Data Scientist role. However, all projects are personal and lack details on team collaboration or real-world impact, which are crucial for assessing cultural fit in a professional environment. The diversity of project domains (NLP, finance, HR, housing) suggests adaptability, but the depth of engagement and problem-solving approach within these projects is not detailed.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate an ability to identify problems and apply data science techniques, but there is no information on collaboration, problem-solving approach, or communication style.