Focus : Data analytics, Business intelligence
Specifications : Business analytics
Skills
Data understanding, Programming, Data visualization, Business Analytics, Problem-Solving, Communication and Presentation Skills,Languages
English, Tamil, Hindi,Hobbies
Chess, Spots, Video games
Certificate
Summary
I am an MBA candidate at CHRIST (Deemed to be University), Pune Lavasa Campus, specializing in Business Analytics. My foundation is in the key components of business analytics and programming languages like R and Python. During my internship at Leap & Scale, I developed personalized widgets using Orange Data Mining, and I am focused on building a career as a proficient business analyst by leveraging data for informed decision-making.
Education History
Internships
During my 2-month internship at Leap and Scale, an IoT-based company, I worked as a Python Developer Intern focusing on creating custom widgets for the Orange Data Mining tool. My main project involved developing tools that facilitate communication with IoT devices through HTTP and MQTT protocols. Initially, I used the PyQt5 library to build various GUI-based tools, which allowed me to familiarize myself with Python's GUI capabilities. Gradually, I moved towards developing dynamic, data-driven widgets within the Orange framework. I created widgets to display real-time device data, charts, and maps while enabling device parameter adjustments through a user-friendly interface. This internship provided hands-on experience with IoT communication, Python libraries, and data analysis, significantly boosting my technical and problem-solving skills.
Projects
The report uses Exploratory Data Analysis (EDA) and a regression model to predict whether breast tumors are malignant or benign based on features from the Breast Cancer Wisconsin Diagnostic Dataset. It involves analyzing tumor characteristics such as radius, texture, and area, performing correlation analysis to identify key relationships among variables, and detecting extreme values through visualizations like histograms and boxplots. A linear regression model is then built using selected features, with the diagnosis as the target variable. The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared, indicating a good fit for predicting tumor diagnosis.
The report conducts Exploratory Data Analysis (EDA) to improve property valuation for IndiaHomes Realty. It analyzes property features like age, price, construction status, number of bedrooms and floors to identify trends. Visualizations such as histograms and box plots reveal that while property prices generally decrease with age, the relationship varies across property types. Additionally, properties with more bedrooms command higher prices. The analysis highlights that factors like location and amenities play a significant role in determining property values, aiming to enhance valuation accuracy for the company.
The report focuses on developing a Python program to manage real estate data, specifically handling property and client information stored in Excel files. The program allows users to display and add property or client data, update property values, and generate reports. It uses Python libraries like pandas for file manipulation and reportlab for report generation. The program is structured into functions and classes to handle property and client data separately. Users interact with the program through a menu with options for managing data and generating PDF reports on property availability and rental income.