Arjun Amla

 Heart_failure_analysis 

 Jun 2022 - Jun 2022 

• An analysis of a public data set to find out the top reasons for a heart attack
• To predict the age group which is more prone to heart attacks
• To visualize the data using Seaborn and matplotlib library

 EDA_on_Zomato_dataset

    Jul 2022 - Jul 2022 

• A detailed analysis on Zomato dataset using pd data frames.
• seaborn and matplotlib are used for data visualization.
• Key observations are drawn out during the analysis

Bank_Marketing EDA

  Aug 2022 - Sep 2022 

• The project is an Exploratory Data Analysis (EDA) of the Bank Marketing dataset from Kaggle.
• The purpose of the project is to gain insights and understand relationships between factors influencing the success of a marketing campaign for a bank.
• The analysis is done using Python libraries such as pandas and matplotlib for data manipulation and visualization.
• The goal is to determine the factors most important in predicting whether a client will subscribe to the bank's product, and the end result is a report summarizing the findings and providing insights for future marketing efforts.

Loan Defaulter detailed 

 Sep 2022 - Oct 2022 

• Objective: Conduct an exploratory data analysis on a loan default dataset to understand the factors that contribute to loan default.
• Data: The dataset contains information about loan applicants and whether they defaulted on their loans.
• Methodology: Perform feature engineering, data visualization, and statistical tests to uncover patterns and relationships in the data.
• Results: Provide insights and information for financial institutions to make better lending decisions and identify risk factors for loan default.
• Tools: Use the Python programming language and libraries such as pandas, numpy, matplotlib, and seaborn to perform the analysis and create visualizations.

Bikeshare problem

Oct 2022 - Oct 2022 

• The project aims to predict the demand for bike sharing services in Washington, D.C. using historical usage patterns.
• The data for the project was collected from the Capital Bikeshare program in D.C. and includes features such as date, time, weather conditions, and seasonal information.
• The project uses machine learning algorithms to analyze the data and make predictions on future demand for bike sharing services.
• The goal of the project is to accurately predict demand, which can help the bike sharing program optimize their resources and improve customer satisfaction.
• The project involves data pre-processing, feature engineering, model training, evaluation, and fine-tuning to achieve the desired results.

Concrete strength Prediction using statsmodel

       Nov 2022 - Nov 2022 

• Objective: To predict compressive strength of concrete based on its ingredients (cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate).
• Data: The project uses a dataset of 1030 records of concrete ingredients and compressive strength available on Kaggle.
• Methodology: Multiple linear regression model is fit using the Statsmodel library in Python to analyze the relationship between predictors (ingredients) and target (compressive strength). Model evaluation is done using R-squared and mean absolute error.
• Results & Conclusion: The regression model shows good fit The model provides a useful tool for concrete engineers to predict concrete strength based on ingredients.

mushroom classification using decision tree

Dec 2022 - Dec 2022 

• The project uses a decision tree algorithm to classify mushrooms as edible or poisonous.
• The dataset used has 8124 instances of mushrooms with 22 attributes, taken from Kaggle.
• The decision tree is implemented using Python and the scikit-learn library. Model accuracy will be evaluated using precision, recall, and F1-score.
• The project's goal is to build a model for mushroom hunters to determine the safety of mushrooms and demonstrate the effectiveness of decision trees in classification problems.