Educational Background

  • Post Graduate Program in Data Analytics Current

    Imarticus Learning, Bangalore, India

  • B.E. Mechanical Engineering

    CGPA: 6.30 (out of 10.0)
    Narayanaguru College of Engineering, Tamil Nadu, India

  • Higher Secondary Education (H.S.E.)

    Percentage: 64.8 (out of 100)
    Sree Chithra Thirunal Residential Central School, Kerala, India

Skills

Field of Interest

- Machine Learning, Data Mining, Artificial Intelligence (AI), Deep Learning, Stock Market, Internet Of Things(IOT) etc.

Programming Languages

- Python, R programming, SQL

Operating System

- macOS, Linux, Windows

Applications

- MS.Excel, Jupyter Notebook, Spyder, R-Studio, MySQL, Overleaf, GitHub

Projects

  • Campus Recruitment Project | GitHub

    May 2021

    • There are so many factors affect the placements of a student for a job. In this project I am trying to understand some of those factors which recruiters consider important.
    • I have also created a machine learning algorithm which classifies the students who have a high chance of getting placed from those who won’t.
    • I have build four models, and based on the model comparison overall accuracy is higher for Random Forest Classifier which is 80% accuracy. All the other models have an overall accuracy of 75.38%.
    • You can find the report of this project here

    Language used: Python
    Development Tools: Jupyter Notebook, MS Word and MS Powerpoint

  • Predicting Term Deposit Subscription By A Client | GitHub

    Mar 2021

    • Marketing campaigns are characterized by focusing on the customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not.
    • A Term deposit is a deposit that a bank or a financial institution offers with a fixed rate (often better than just opening a deposit account) in which your money will be returned back at a specific maturity time.
    • The objective of this project is to predict if a customer subscribes to a term deposits or not, when contacted by a marketing agent.
    • An interesting data exploration shows that, number of calls made by the marketing agent and the number of subscription to term deposit are positively correlated.
    • Due to the target variable imbalance, oversampling using SMOTE algorithm was done.
    • A Logistic regression model and a Support Vector Machine (SVM) was trained of which Support Vector Machine gave a higher recall of 87.35%

    Language used: Python
    Development Tools: Jupyter notebook

  • Property Price Prediction | GitHub

    Jan 2021

    • A key challenge for property sellers is to determine the sale price of the property.
    • The ability to predict the exact property value is beneficial for property investors as well as for buyers to plan their finances according to the price trend.
    • There are 80 variables which focus on the quality and quantity of many physical attributes of the property.
    • I used Python programming language to do the Exploratory Data Analysis on the data set and selected only relevant features to create a Linear Regression model.
    • The final model yields an r2-score of 75.08% with only 28 independent variables.
    • You can find the report of this project here

    Language used: Python
    Development Tools: Jupyter Notebook