April 25, 2023
Welcome to the Stock Prices Prediction project – a powerful initiative designed to empower investors with
accurate insights for informed decision-making in the dynamic world of finance. In this project, I built a stock price prediction
model using two different machine learning
models.
The primary goal of this project is to predict future stock prices using advanced machine learning techniques. By harnessing the
power of Ridge linear regression and LSTM-based deep neural networks, the project aims to provide investors with reliable predictions.
The ultimate objective is to assist investors in maximizing returns and making strategic decisions on buying and selling securities.
Welcome to the Churn Modeling ANN project, where the primary goal is to leverage Artificial Neural Networks (ANN) to predict customer
churn in the banking sector. This predictive model aims to assist in the decision-making process surrounding loan approvals, providing
insights into whether a customer is likely to leave the bank or stay. In this project, I built a deep learning solution using Artificial Neural
Network to determine whether a customer will leave a bank or not.
The project utilizes a comprehensive bank customer dataset sourced from Kaggle. This dataset comprises crucial details such as credit scores,
location, gender, age, duration of the customer's relationship with the bank, and account balance. These features play a vital role in determining
customer behavior and, subsequently, the likelihood of churn.
Welcome to the Investigate_FBI_Gun_Data project, a comprehensive exploration of the relationship between FBI gun data and US census data using Python.
The project delves into datasets from the Federal Bureau of Investigation's (FBI) criminal background check system and the US Census Bureau, unraveling
insights into the dynamics between firearm statistics and population demographics. In this project, I used Python to perform a Investigative analysis on
the FBI gun data in conjunction with the US census data.
Welcome to the US Crime Data Analysis and Visualization project, where we delve into the extensive crime data spanning from 1980 to 2014 in the United States.
This project is hosted on GitHub and utilizes Microsoft Power BI for in-depth analysis and visualization. The Power Query editor takes center stage in the ETL
process, focusing on transforming and cleaning the data to extract meaningful insights.In this project, I built a multiple-page report using Power BI to visualize
crime occurrences and how crime rate has been spread from 1980 to 2014 in the US.
The primary goal of this project is to provide a comprehensive and visually engaging exploration of US crime data. By leveraging the capabilities of Power BI, we aim
to offer insights into crime patterns over the specified timeframe. The project's purpose extends to fostering a better understanding of crime trends, aiding in informed
decision-making and policy formulation.
Welcome to the AI Ethics - Personalized Budget Prediction Model project, where the focus is on building an ethical AI model for a personalized "activity recommender" within
a hypothetical use case. The project addresses the medium ethical AI risk associated with a synthetic dataset. In this project, I built an ethical AI solution using Python
and the IBM AIF360 toolkit to make personalized budget prediction as part of a mobile app.
IDOOU is a mobile app designed to provide users with personalized recommendations for activities in a given area, considering factors such as education level, age, and gender.
This project aims to predict the budget of users based on these factors and evaluate the fairness and bias issues associated with the model.
IDOOU's creators want to understand if users with higher education credentials, specifically bachelor's and master's degrees, belong to a privileged group with a budget >= $300
compared to users who graduated from high school. The project involves designing an AI model to predict users' budgets and conducting a comprehensive analysis of fairness and bias
issues in the provided data.
Welcome to the Financial Dashboard Modeling project, a compelling exploration where the power of Power BI was harnessed to craft an in-depth financial report. This endeavor prominently
featured the extensive use of DAX (Data Analysis Expressions) language for creating measures and M language for meticulous data transformation within the Power Query Editor. For this project,
I used Power BI in building a financial report. I utilized my knowledge of DAX language in coming up with measures, and M language in transformation of the data in the Power Query Editor.