SAMBHAV CHATHLY

Analytical and goal-oriented professional with a strong foundation in critical thinking and problem-solving. Skilled in utilizing analytical tools such as SPSS, R-Studio, Python, Visualization and Analytics. Experienced in financial reporting and data analysis from internships, with a demonstrated ability to work collaboratively and lead initiatives. Proactive involvement in extracurricular activities, including serving in leadership roles and participating in community projects. Passionate about music and sports. Eager to leverage my skills and contribute to organizational success.

Title: Market Analysis and Consumer Behavior Study in the Organic Food Sector

Description: Conducted a comprehensive market analysis and consumer behavior study within the organic food sector. Utilized quantitative research methodologies to gather and analyze data on factors influencing consumer preferences, sectoral growth trends, and the economic impact of organic farming. Implemented Google Forms questionnaire distributed across various social media platforms to collect data from 141 participants. Employed statistical techniques such as descriptive statistics, chi-square tests, one-way ANOVA, linear regression, and clustering analysis to derive insights. The project involved reviewing existing literature to inform the study framework and conducting SWOT and BCG matrix analyses to assess sectoral strengths, weaknesses, opportunities, and threats. Key findings contributed to understanding consumer behavior, market dynamics, and policy implications in the organic food industry.

Skills Utilized:

• Quantitative Research
• Data Collection and Analysis
• Statistical Analysis
• Literature Review
• Market Analysis
• Consumer Behavior Study
• SWOT and BCG Matrix Analysis
• Report Writing and Presentation Skills

Title:Analytical Investigation of Loan Applicants: A Statistical ApproachAnalytical Investigation of Loan Applicants: A Statistical Approach

• Analysed loan applicant data using Python to reveal insights into loan approval and demographic trends. Key analyses included age comparisons, gender-based loan amounts, loan uptake patterns, geographical distribution, and gender equality in loan approval.
• Outcome: Provided actionable insights into loan approval processes and demographic trends, enhancing analytical proficiency.
• Tools/Languages Used: Python, Pandas, SciPy, Researchpy
• Analysed loan applicant data using Python to reveal insights into loan approval and demographic trends. Key analyses included age comparisons, gender-based loan amounts, loan uptake patterns, geographical distribution, and gender equality in loan approval.
• Outcome: Provided actionable insights into loan approval processes and demographic trends, enhancing analytical proficiency.
• Tools/Languages Used: Python, Pandas, SciPy, Researchpy
• Skills: python · Statistics · Banking · Python (Programming Language) · SciPy · Statistical Data Analysis

Title: Data Analysis and Machine Learning Project on TitanicData Analysis and Machine Learning Project on Titanic

• Utilized Python and various libraries such as pandas, numpy, seaborn, and plotly to perform exploratory data analysis (EDA) on the Titanic dataset, containing passenger information.
• Performed data preprocessing tasks such as handling missing values by imputing median values for the 'Age' column and mode values for the 'Cabin' and 'Embarked' columns.
• Identified and treated outliers in numerical features ('Age' and 'Fare') using the interquartile range (IQR) method.
• Visualized the distribution of categorical variables ('Survived', 'Pclass', and 'Sex') using bar charts and pie charts to gain insights into the dataset.
• Calculated the correlation matrix and visualized it using a heatmap to understand the relationships between different features.
• Implemented a Decision Tree Classifier using scikit-learn to predict survival outcomes ('Survived') based on passengers' ages.
• Applied the trained model to make predictions on a test dataset and evaluated the model's performance by comparing predicted outcomes with actual values.

Title: Predicting Customer Lifetime Value (CLV) in Online RetailPredicting Customer Lifetime Value (CLV) in Online Retail

In this project, we aim to predict Customer Lifetime Value (CLV) for an online retail business using machine learning techniques. CLV is a crucial metric for businesses as it represents the total revenue a company expects to earn from a customer throughout their entire relationship. By predicting CLV, businesses can identify high-value customers, tailor marketing strategies, and optimize customer acquisition and retention efforts.

Title: Understanding Gradient Boosting AlgorithmUnderstanding Gradient Boosting Algorithm

• In this project, we explore the Gradient Boosting Algorithm, a powerful ensemble learning technique, through both theoretical understanding and practical implementation using the Scikit-learn library in Python.
  Steps in Implementation:
• Data Preparation: We load the breast cancer dataset, preprocess the features, and encode the target variable.
• Feature Scaling: StandardScaler is applied to standardize the feature values to improve model performance.
• Model Training: We split the dataset into training and testing sets and train the Gradient Boosting Regression model with specified hyperparameters.
• Model Evaluation: We evaluate the model's performance using metrics such as R-squared, Mean Squared Error (MSE), and accuracy scores on both training and testing datasets.

Title: Value at Risk (VaR) Analysis Using Different MethodsValue at Risk (VaR) Analysis Using Different Methods

In this analysis, we perform Value at Risk (VaR) analysis using three different methods: Historical Method, Parametric Method, and Monte Carlo Simulation. VaR is a measure used to assess the potential loss in value of a portfolio over a specified period and at a given confidence level. These methods help investors understand the downside risk associated with their investment portfolios.

Sarvahitey- Gurgaon/Noida- December 2022

· Led the Paper Bridge project, collecting books valued at INR 8400 in a month

· Recorded children's information using Python

· Organized basic hygiene workshops

· Served as an educator to underprivileged children

· Sorted donated books by genre

· Skills: Teamwork, Python, Problem Solving