Projects

Bank Management System

Project Description:

The Bank Management System is a console-based application designed to simulate some of the basic operations that can be performed in a bank. While it's a simplified representation of a real banking system, it serves as a learning tool for understanding the fundamental concepts of data management and user interaction.

Key Functionalities:

Account Creation: Users can create new bank accounts by providing essential information such as their account number, name, account type (Checking or Savings), and an initial deposit amount. This feature mimics the process of opening a bank account in a real bank.

Deposit and Withdrawal: Account holders can deposit money into their accounts or withdraw funds from their accounts. When depositing, they specify the amount they want to add, and when withdrawing, they specify the amount they want to take out. The system ensures that withdrawals do not exceed the available balance.

Balance Enquiry: Account holders can check their account balances at any time by entering their account number. This feature provides them with their current account balance, which is essential for keeping track of their finances.

Account Listing: The system maintains a list of all the bank account holders. Users can request a list of all account holders, displaying their account numbers, names, account types, and balances. This feature helps bank staff and account holders get an overview of all accounts.

Account Closure: Users have the option to close their bank accounts. This operation removes the account from the system, effectively closing it. To perform this action, users need to specify the account number of the account they want to close.

Account Modification: Account holders can modify their account details, including their name, account type, and balance. This feature is useful for updating account information when needed.

Technical Details:

Data Storage: The system stores account data in a file named "accounts.data" using the pickle library. This file acts as a simple database for storing and retrieving account information. However, it's important to note that real banks use more sophisticated databases and security measures for data management.

Menu-Driven Interface: The system presents a user-friendly menu that allows users to select the desired operation by entering a corresponding number. This menu-driven approach makes it easy for users to interact with the system.

Project flow:

Project Overview:

User Interaction: Users access the system through a web browser.

Web Server: Flask, a Python web framework, hosts the system on a server.

Bank Logic: Python code handles core banking logic, such as creating accounts and processing transactions.

HTML Templates: These templates structure the user interface and display data.

Flow: Users click links, triggering HTTP requests to Flask. Flask routes requests to the code, processes them, and returns results via HTML templates.

Result: Users see the outcome of their banking actions in the web browser.

Key skills:

Programming Languages (e.g., Python)

Web Development (Flask, HTML/CSS)

Database Management (SQL)

Data Handling (File I/O, Data Structures)

Object-Oriented Programming (OOP)

User Interface (UI) Design

Problem Solving

Version Control (Git)

Security Awareness (Data Encryption, Secure Coding)

Testing and Debugging

Documentation

Project Management

Communication

Deployment and Hosting

Knowledge of Banking Systems

Compliance and Regulations

Adaptability

Troubleshooting

Time Management

Quality Assurance

Production Challenges:

Security: Ensuring data and transaction security.

Scalability: Handling increasing user and transaction loads.

Data Integrity: Preventing data corruption and maintaining consistency.

High Availability: Ensuring 24/7 system availability.

Performance: Optimizing for responsiveness.

User Experience: Providing an intuitive interface.

Regulatory Changes: Adapting to evolving regulations.

Data Backup: Protecting against data loss.

Customer Support: Efficiently handling inquiries and issues.

Fraud Prevention: Detecting and preventing fraud.

Maintenance: Regular system upkeep and updates.

Integration: Securely connecting with external systems.

Training: Educating staff and users.

Data Privacy: Protecting user privacy and data.

Audit Trails: Maintaining detailed user activity logs.

User Adoption: Encouraging system use.

Regulatory Reporting: Accurate and timely reporting.

Customization: Meeting diverse user needs.

Troubleshooting:

In short, troubleshooting in a Bank Management System project involves addressing issues related to login, transactions, data integrity, performance, security, user interface, error handling, dependencies, deployment, user support, regression testing, and compliance with financial regulations. Timely resolution is critical for system reliability and security.

Remember when you were leisurely browsing Amazon.com and Ebay to find that perfect gift for others (or yourself)? How often do you type in the search box, click on the navigation bar, expand product descriptions, or add a product to your cart? If you were an e-commerce company, every one of these actions can become the key to optimizing the entire shopping experience. And thus, the daunting tasks of collecting, processing, and analyzing shoppers’ behavior and transaction data open up enormous opportunities for big data in e-commerce.

A powerful big data analytics platform allows e-commerce companies to :

(1) clean and enrich product data for a better search experience on both desktops and mobile devices; and

(2) use predictive analytics and machine learning to predict user preferences through log data, then personalize products in a most-likely-to-buy order that maximizes conversion. There has additionally been a new movement towards real-time e-commerce personalization enabled by big data's massive processing power.

Project Flow:

Project Overview:

1)  Upstream source inserts new data and updates existing data continuously.

2)  Mysql database a downstream source, which is linked with upstream source, and fetching all the data into tables.

3)  Sqoop extracts or pool the table information from one database(oracle) to another database(hdfs).

4)  Slowly Changing dimension phase has a two subdirectory in target path active and closed, active subdirectory is keeping old records as well as new records, closed subdirectory doesn’t has any new records.

5)  HDFS Staged data loaded into Staging Hive table. To keep the uniqueness of records newly inserted, updated and unique records will be inserted into new Main Hive table.

6)  Main Hive table integrates with Elasticsearch and Main Hive table inserts records as documents into Elasticsearch for faster searches over huge data.

7)  Elasticsearch with Kibana allows you to query, filter and aggregate the data as per business reqirement.

8)  Kibana visualize provides Visualization of the processed data to the Business users.

Analytics with Elasticsearch & Kibana:

Elasticsearch is an open-source, very highly scalable, full-text search and analytics engine.It allows you to store, search, and analyze big volumes of data quickly and in near real time.It can be integrated with Hadoop/Hive for very fast query results.Kibana is an open source analytics and visualization platform designed to work with Elasticsearch that allow you to visualize data in a variety of charts, bars, tables etc.

Visualization with Kibana:



Key skills:

•   Sqoop
•   Hive
•   Hadoop
•   HDFS
•   Elasticsearch with Kibana
•   Mysql

Production Challenges:

• Hive tables and Elasticsearch/KibanaIntegration error due to default configurations in Elasticsearch/Kibana.
• Huge records needs multiple mappers.
• Multiple exceptions occurred while quering Hive-Elasticsearch table.

Troubleshooting:

•  Elasticsearch-Hive integration using EsStorageHandler elasticsearch-hadoop-hive.jar for data indexing into Elasticsearch directly through a Hive table.
•  Proper configurations made inside elasticsearch.yml file in Elasticsearch/config folder as well as kibana.yml file in Kibana/config folder for clean data indexing.

************** Thank You *************

Healthcare Data Analysis

Project Description:

‘Big data’ is massive amounts of information that can work wonders for business. It has become a topic of special interest for the past some decades.Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide to their customers so that they can their futuristic business goals.

In the healthcare industry, various sources for big data include hospital records, medical records of patients and devices that are a part of internet of things. Biomedical research also generates a important portion of big data relevant to public healthcare. This data requires proper management and analysis to derive meaningful information.

There are various challenges associated with each step of maintaining big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide solutions for improving public health, healthcare providers are need to be fully equipped with appropriate infrastructure to systematically store & analyse big data. An efficient management and analysis of big data can change the game by opening new avenues for modern public healthcare. That’s exactly why various healthcare industries are taking strong steps to convert this potential into better services and financial advantages for their businesses.

Project Flow:

Project Overview:

1)   Data coming from different sources like Webservers, IOT devices and Mobile Applications are combined together using REST API. And then connected to kafka server.

2)   Spark integrates with Zookeeper, Kafka Utilities and sends the data to the Kafka producer for further operations on the data.

3)   Producer pushes the data to the Kafka broker. Kafka Cluster (broker) receives this data also creates topics for the data. broker’s role is to balance the load here. Spark code divides into micro batches of streams called DStreams and process the data in real-time.

4)    Kafka Consumer pulls these DStreams from the kafka queue & Spark code performs DStream Operations on each & every DStream in real-time.

5)   At the end, Spark integrates with Cassandra database and stores the desired data in Cassandra in real-time.

Real Time Data Analysis with Kafka :

Kafka is an open source software which provides a framework for storing and analysing streaming data.

Kafka is designed to be run in a “distributed” environment, which means it runs 

across several servers, leverages the additional processing power and storage 

capacity that this brings.

Kafka is used in order to stay competitive basically. Businesses today rely on real-time data analysis allowing them to gain faster insights. Real-time insights allow businesses or organisations to make predictions about what they should stock, promote, or pull from the stock, based on the most up-to-date information possible.

Why Cassandra?

Cassandra is a distributed NoSQL database management system which is open source with wide column store, allows us to handle& maintain large amount of data across many commodity servers provides high availability and no single point of failure.. It is written in Java language and developed by Apache Software Foundation.

Avinash Lakshman & Prashant Malik initially developed the Cassandra at Facebook to power the Facebook inbox search feature.Due to its outstanding technical features Cassandra becomes so popular.

Cassandra is a write intensive NoSQL database. It’s write performance is higher than most other Nosql databases. Cassandra follows a peer to peer architecture and opposed to master-slave architecture of MongoDB and most RDBMS. That means you can write to any peer in the cluster and Cassandra will take care of data synchronization.

Key skills:

•  Apache Spark
• Spark Streaming
•  Kafka
•  Apache Cassandra
• Scala
• Mysql

Production Challenges:

•      Apache Spark & Kafka ClusterIntegration error due to default configurations and IP_Adress,Port issues.
•     Cassandra maintainance issues and Cassandra.yml file configurations issues faced.
•    Multiple exceptions occurred duringSpark Dstream operations.

Troubleshooting:

•    Assigned proper configuration settings in Cassandra.yml file to integrate with spark.
•   Selected SBT config’s very wisely for every Spark-scala code.

************** Thank You *************

Description:

Property rental prices are a key economic indicator, often signaling significant changes in things like unemployment rate or income. Accurately predicting rental prices would help organizations offering public and commercial services with the ability to better plan for and price these services.

 Monthly rental values for properties vary due to a broad mix of factors. Some measures are objective, like Location, Number of Bedrooms(BHKs), Furnished or Unfurnished, Area of the flat in SqFt’s, Age of the property and on which floor flat is.

The rental market in Bangalore is unusually diverse and difficult to predict due to the region's varied landscape and large, widely spread population.

Currently, automated valuation models are used for over 90% of residential property estimates Bangalore. Using data on location, property, zoning, past sales, and more, the goal of this project is to estimating the monthly market rental value for residential properties in the area of Bommanahalli and Whitefield area of Bangalore.

Roles and Responsibilities:

Entire Work was divided between 7 teams.

1.Team 1 is dedicated toward collecting the data from different sources like from websites like

www.99acres.com

www.magicbricks.com

www.nobroker.com

2.Team 2 is dedicated with responsibility of importing data from different sources and cleaning the dataset using numpy, pandas, statsmodels and sklearn and bring the dataset to ideal format Training a machine learning model.

3.Team 3 is responsible for identifying the best suitable machine learning models as per the dataset and training the model according to dataset. Team is also responsible for optimizing the model to its best accuracy using different hyperparameter training methods.

4.Team 4 is dedicated towards testing the model inoder to get the best Machine Learning model.

5.Team 5 is responsible for Creating User Interface for the models according to the fields passed into model while training.

6.Team 6 is responsible for creating backend code and connect UI to the model.

7.Team 7 is responsible for creating REST services using Flask frame work in Python and connect it to the model.

KeySkills:

1.      Hadoop, Hive for data collection and storage

2.      Python, numpy, pandas, sklearn, statmodels for data cleaning

3.      Html5, Css3 and Bootstrap for UI designing

4.      Python for backend coding

5.      Sklearn for model training

6.      Flask frame work for Creating REST api

Problems and Troubleshooting:

1.      Collect and combine data from different sources

2.      Remove missing values and irrelevant data in different columns like character value in numerical data containing columns.

3.      Identifying best Character Encoding model for the converting character to machine readable format dataset for training a machine learning model.

4.      Reducing the overfitting of the model giving irrelevant outputs

5.      Identifying best feature selection techniques from wrapper, filter and embedded methods

6.       Determining Best model for Machine Learning

7.      Inserting the data from UI according to the data type required

8.      Manage the number of inputs from UI to the number of input actually model requires.

Project Description

The FMCG Data Analysis and Visualization project aim to apply advanced data science techniques to a dataset encompassing Fast-Moving Consumer Goods (FMCG). The primary objective is to extract actionable insights that can inform strategic decision-making within the FMCG industry. Leveraging Jupyter Notebooks and Python libraries, the project encompasses a comprehensive data science workflow, from initial data exploration to machine learning-driven analysis and visualization.

Project Flow:

Project Overview:

Step 1: Define Project Goal

Objective: Extract insights from SAP data related to Fast-Moving Consumer Goods (FMCG) to optimize sales, identify product preferences, and enhance inventory management.

 Step 2: Data Collection from SAP

Connect to SAP HANA Database:

Utilize appropriate Python libraries (e.g., hdbcli) to establish a connection to the SAP HANA database.

Retrieve relevant FMCG data from SAP tables or views.

Step 3: Data Cleaning in Pandas

Data Cleaning and Preprocessing:

Use Pandas for cleaning tasks, addressing missing values, removing duplicates, and handling outliers.

Ensure data consistency and prepare it for further analysis.

Step 4: Data Visualization in Seaborn

Data Exploration and Visualization:

Leverage Seaborn and Matplotlib to conduct exploratory data analysis (EDA).

Create visualizations to understand distributions, trends, and relationships within the data.

Step 5: Encoding in SciKit-Learn

Encoding Categorical Variables:

Utilize SciKit-Learn's preprocessing module to encode categorical variables if needed for machine learning models.

Step 6: Feature Selection in SciKit-Learn

Feature Selection:

Use SciKit-Learn's feature selection techniques (e.g., SelectKBest, RFE) to identify relevant features for modeling.

Step 7: Data Splitting in SciKit-Learn

Data Splitting:

Using the train_test_split method in SciKit-Learn, divide the dataset into training and testing sets.

Step 8: Scaling in SciKit-Learn

Feature Scaling:

Apply feature scaling using SciKit-Learn's preprocessing methods (e.g., StandardScaler, MinMaxScaler) to ensure numerical features are on a similar scale.

Step 9: Model Training in SciKit-Learn

Model Training:

Choose a suitable machine learning model (e.g., regression, classification) from SciKit-Learn.

Train the model using the training dataset.

Step 10: Model Evaluation in SciKit-Learn

Model Evaluation:

Evaluate the model's performance using the testing set and relevant metrics (e.g., accuracy, precision, recall) from SciKit-Learn.

Step 11: Presentation in PowerPoint

Presentation of Results:

Create a PowerPoint presentation summarizing key findings, insights, and visualizations.

Use visualization tools (Matplotlib, Seaborn) and export visualizations for inclusion in the presentation.

Tools and Technologies

SAP HANA Database Connection: Utilize hdbcli or other SAP HANA drivers for data retrieval.

Pandas: For data cleaning and preprocessing.

Seaborn and Matplotlib: For data exploration and visualization.

SciKit-Learn: For encoding, feature selection, data splitting, scaling, model training, and evaluation.

PowerPoint: For the presentation of results.

Keyskills:

·        Database Connectivity:

·        Data Cleaning and Preprocessing

·        Data Visualization

·        Machine Learning

·        Feature Engineering

·        Statistical Analysis

·        Presentation Skills

·        Problem Solving

·        Documentation

·        Domain Knowledge (FMCG)

·        Communication

·        Critical Thinking

·        Python Programming

·        Version Control

·        Adaptability

Tools:

Abstract

This project focuses on predicting airline ticket prices based on various journey-related attributes using machine learning techniques. The dataset contains information such as airline names, departure and arrival times, journey durations, number of stops, and ticket prices. Two regression models—Linear Regression and Random Forest Regression—were implemented to analyze the data and predict ticket prices. Random Forest Regression outperformed Linear Regression in terms of accuracy and reliability. It was selected for deployment to assist travelers in identifying optimal booking times and enable travel agencies to implement effective dynamic pricing strategies.

Overview

Airline ticket prices are highly dynamic and influenced by various factors such as the time of booking, travel duration, and flight stops. Accurate prediction of ticket prices can significantly benefit customers and travel agencies alike. This project leverages a dataset comprising flight details to build a predictive model. By employing machine learning techniques, the project aims to forecast ticket prices with high precision, enabling informed decision-making for both travelers and service providers.

Problem Statement

Traditional methods for analyzing airline ticket pricing rely on historical trends and do not account for multiple influencing factors in real time. This often results in suboptimal predictions, leading to either customer dissatisfaction or revenue loss for travel agencies. This project addresses the need for a robust machine learning-based model that can predict ticket prices dynamically and accurately, considering multiple attributes simultaneously.

Project Flow

Dataset Description

The dataset contains 10,683 entries with the following attributes:

1. Airline: The airline operating the flight.
2. Date_of_Journey: The date when the journey takes place.
3. Source: The city where the journey starts.
4. Destination: The city where the journey ends.
5. Route: The flight path taken during the journey.
6. Dep_Time: The departure time of the flight.
7. Arrival_Time: The arrival time of the flight.
8. Duration: The total travel time.
9. Total_Stops: The number of stops in the journey.
10. Additional_Info: Additional information about the flight, if any.
11. Price: The ticket price, serving as the target variable for prediction.

Data Preprocessing

1. Handling Missing Values:
2. Entries with missing or incomplete data, particularly in the "Route" and "Total_Stops" columns, were removed to ensure dataset consistency.
3. Feature Engineering:

• Extracted numerical and categorical features from "Date_of_Journey", "Dep_Time", and "Arrival_Time".
• Derived additional features such as travel month, day, and hour to identify trends in pricing.
• Converted "Duration" into numerical values (hours and minutes) for better compatibility with models.

1. Encoding Categorical Variables:
2. Categorical features like "Airline", "Source", "Destination", and "Total_Stops" were encoded using one-hot encoding to ensure seamless integration into regression models.

Model Selection

Two machine learning models were explored to predict ticket prices:

1. Linear Regression:
2. A straightforward model used as a baseline to understand the dataset's predictive potential. Although simple to implement, it struggled to capture the complex relationships in the dataset, resulting in suboptimal predictions.
3. Random Forest Regression:
4. A robust ensemble method that builds multiple decision trees and averages their predictions. This model effectively handled the dataset's non-linearity and interactions between features, making it the superior choice.

Model Evaluation

The Random Forest Regression model demonstrated the best performance based on the following metrics:

1. Mean Absolute Error (MAE): Indicates the average difference between actual and predicted ticket prices.
2. Mean Squared Error (MSE): Evaluates the average squared differences to penalize large errors.
3. R-squared Score: Measures how well the model explains the variance in the data.

Random Forest Regression consistently outperformed Linear Regression across these metrics, establishing it as the preferred model for deployment.

Results

The Random Forest Regression model provided accurate predictions for airline ticket prices, with key outcomes including:

• High Precision: Captured subtle variations in ticket pricing based on journey details.
• Improved Reliability: Reduced prediction errors compared to traditional models.
• Scalability: Efficiently handled the dataset's size and complexity, ensuring robust performance.

Model Deployment

The Random Forest Regression model was deployed for real-time ticket price prediction. Key features of the deployment include:

• Dynamic Predictions: The model forecasts ticket prices instantly based on user-provided journey details.
• User-Friendly Interface: Integrated into a web-based application to allow travelers to compare prices across airlines.
• Business Utility: Enables travel agencies to optimize pricing strategies dynamically.

Future Work

To further enhance the model's performance, future work will focus on:

1. Integrating external factors such as weather conditions and special events to improve prediction accuracy.
2. Implementing deep learning techniques for better handling of large-scale and complex datasets.
3. Exploring time series analysis to identify seasonal trends and patterns in ticket prices.

Skills Acquired

• Python programming
• Data preprocessing and cleaning
• Feature engineering
• Exploratory Data Analysis (EDA)
• Machine learning (Linear Regression, Random Forest Regression)
• Model evaluation and validation
• Deployment of machine learning models
• Use of libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn

Conclusion

This project demonstrates how machine learning, specifically Random Forest Regression, can effectively predict airline ticket prices. The model's deployment offers practical benefits for both customers and businesses by facilitating dynamic and accurate price forecasts.

Abstract

This project aims to identify fraudulent credit card transactions using machine learning techniques. The dataset contains various features, including customer details, transaction information, merchant data, and geographic coordinates. Two models—Decision Tree and Random Forest—were implemented for fraud detection. Among these, Random Forest outperformed with higher accuracy and was selected for deployment in a real-time system to promptly detect suspicious activities and mitigate financial risks for banks and credit card companies.

Overview

Credit card fraud poses a significant challenge to financial institutions, resulting in substantial financial losses. Early detection is essential to prevent these losses while maintaining trust and security for customers. This project leverages a dataset with both genuine and fraudulent transactions to develop a reliable fraud detection system. Machine learning models are employed to improve detection accuracy, ensuring timely actions and risk reduction.

Problem Statement

Without advanced fraud detection mechanisms, fraudulent activities can go unnoticed, causing significant damage. Traditional rule-based approaches struggle to keep up with rapidly changing fraud tactics due to their limited flexibility. This project aims to develop a machine learning-based model capable of learning new fraud patterns over time, ensuring it effectively distinguishes between legitimate and fraudulent transactions for proactive risk management.

Project Flow

Dataset Description

This dataset contains essential features related to credit card transactions. and overview of key attributes:

trans_date_trans_time: Records the date and time when the transaction took place.

cc_num: A partially masked or anonymized credit card number for privacy.

merchant: Name of the merchant involved in the transaction.

category: Describes the type of transaction (e.g., groceries, entertainment).

amt: The monetary value of the transaction.

first, last: The first and last names of the cardholder.

gender: The gender of the cardholder.

city_pop: Population of the city where the transaction was processed.

job: The occupation of the cardholder.

dob: The cardholder's date of birth.

lat, long: The geographical coordinates (latitude and longitude) of the cardholder’s location.

merch_lat, merch_long: The latitude and longitude of the merchant’s location.

is_fraud: A binary indicator showing whether the transaction is fraudulent (1) or legitimate (0).

Data Preprocessing

Handling Missing Values:

Any missing or incomplete data entries were removed to maintain the reliability and consistency of the dataset.

Feature Engineering

Additional features were created by leveraging transaction amount, time intervals, and the geographical distance between the cardholder and merchant, enhancing the model's ability to detect fraud patterns.

Encoding Categorical Variables

Categorical features such as gender, job, and transaction category were transformed into numerical values to ensure compatibility with machine learning algorithms.

Model Selection:

Two supervised learning models were employed: Decision Tree and Random Forest.

Decision Tree: This model splits data based on feature conditions to build a tree-like structure. However, it tends to overfit the training data, reducing its generalization ability.

Random Forest: As an ensemble technique, it aggregates predictions from multiple decision trees to improve accuracy and minimize overfitting by averaging results.

Model Evaluation

The Random Forest model showed better accuracy and generalization compared to the Decision Tree, making it the ideal choice for deployment in real-time fraud detection systems.

Results

The Random Forest model achieved the highest accuracy for detecting fraudulent transactions. Key performance metrics include:

• Precision: High precision minimizes false positives, ensuring that legitimate transactions are not mistakenly flagged as fraudulent.
• Recall: A high recall rate ensures the model effectively captures most fraudulent transactions.
• F1 Score: This metric, the harmonic mean of precision and recall, offers a balanced measure of the model’s performance.

Model Deployment

With the Random Forest model showing superior results, it was chosen for deployment to evaluate transactions in real-time. The deployment involves:

• Real-Time Predictions: The model assesses transactions as they happen, instantly flagging suspicious activities.
• Alerts: Transactions identified as high-risk generate alerts, prompting immediate investigation by the fraud detection team.
• Scalability: The Random Forest model’s ability to run multiple decision trees in parallel makes it well-suited for handling high transaction volumes efficiently.

Future Work

Future improvements will focus on implementing advanced techniques, such as neural networks, to further enhance detection accuracy. We also plan to analyze transaction sequences using time series methods, which may uncover new fraud patterns and improve the model’s performance.

Skills Acquired

• Python programming
• Data preprocessing and cleaning
• Feature engineering for fraud detection
• Encoding categorical variables
• Machine learning (Decision Tree, Random Forest)
• Model evaluation using metrics like Precision, Recall, and F1 Score
• Deployment of machine learning models for real-time systems
• Use of Python libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib
• Understanding fraud detection strategies in financial systems

Conclusion

The use of machine learning, particularly the Random Forest model, has proven highly effective in detecting fraudulent credit card transactions. This project demonstrates how leveraging advanced algorithms can significantly enhance fraud detection capabilities, enabling financial institutions to mitigate risks and safeguard customers more effectively.

E-Commerce Data Analytics

Abstract

This project explores smartphone data from an e-commerce platform to uncover trends in pricing, brand popularity, and feature-specific patterns. By employing systematic data cleaning, feature engineering, and exploratory analysis, the project aims to provide actionable insights for businesses and consumers. Key outcomes include price categorizations, battery capacity trends, and brand-specific comparisons.

Overview

The availability of diverse smartphone options on e-commerce platforms makes decision-making complex for buyers and sellers. This project utilizes data analytics techniques to analyze and refine smartphone data, offering clarity on key market trends. The analysis includes extracting brand details, price categorization, and feature-specific visualizations.

Problem Statement

E-commerce platforms provide extensive data on smartphones, but this data often requires preprocessing and analysis to extract meaningful insights. This project addresses the following:

1. Cleaning and preparing raw data for analysis.
2. Identifying significant trends in pricing, brands, and features.
3. Presenting insights in an actionable format to assist businesses and consumers.

Project Flow

1. Data Loading:

• Imported the dataset using Pandas and NumPy.
• Inspected the data structure to identify columns, datatypes, and missing values.

1. Data Inspection:

• Checked column names, data types, and row counts.
• Investigated missing or inconsistent data entries for correction.

1. Data Cleaning:

• Dropped irrelevant or incomplete rows/columns.
• Applied regex techniques to remove special characters and standardize data.

1. Feature Extraction:

• Brand Extraction: Derived Phone_Brand by extracting brand names from the Phone_Name column.
• New Features:
• Combined RAM and storage details into a single attribute.
• Categorized prices into Budget, Mid-Range, Premium, and High-End.

1. Final Cleaned Dataset:

• Displayed the refined dataset for further analysis.
• Used the cleaned data for visualizing trends and extracting insights.

Key Insights and Visualizations

1. Brand Analysis:

• Counted and listed unique phone brands.
• Identified the top 3 most common brands and visualized their frequency using a pie chart.

1. Battery Capacity Trends:

• Analyzed the distribution of battery capacities using a histogram.
• Highlighted phones with high-capacity batteries and their price ranges.

1. Price Analysis:

• Categorized phones into price tiers (Budget, Mid-Range, Premium, High-End) and visualized the distribution using a bar chart.
• Compared the average price per brand using a bar chart.

1. Display Trends:

• Identified the most common display sizes and types.
• Compared average prices of phones based on display types (e.g., HD, Full HD).

Skills Acquired

• Data loading, cleaning, and inspection.
• Feature engineering using Python (Pandas, NumPy).
• Data visualization with Matplotlib and Seaborn.
• Extracting actionable insights from raw data.

Conclusion

The structured analysis of e-commerce smartphone data reveals key trends, including brand popularity, price categorizations, and feature-based insights. The findings are valuable for both businesses optimizing product offerings and consumers making informed purchase decisions. This project exemplifies how systematic data preparation and visualization can transform raw data into meaningful insights.

Abstract

This project involves the analysis of an insurance dataset to uncover trends in BMI, smoking habits, regional insurance charges, and the impact of demographic and health-related attributes on charges. By leveraging data preprocessing, feature engineering, and visualization techniques, the project aims to provide valuable insights for understanding the factors influencing insurance costs and customer segmentation.

Overview

Insurance companies rely on historical data to price premiums accurately, but extracting meaningful insights from such datasets requires detailed analysis. This project uses exploratory data analysis (EDA) to address key questions related to BMI, smoking habits, regional impacts, and demographic patterns. The goal is to provide actionable insights for insurance providers to optimize pricing strategies and enhance customer targeting.

Problem Statement

Analyzing insurance datasets is crucial for identifying patterns that influence premium costs. Factors such as BMI, smoking habits, and regional differences often exhibit significant effects on insurance charges. The objective of this project is to:

1. Investigate the relationship between key attributes (e.g., BMI, smoking habits) and insurance charges.
2. Identify regional and demographic patterns in premium pricing.
3. Provide visualizations to effectively communicate findings.

Project Flow

1. Data Loading:

• Imported the dataset using Pandas and NumPy.
• Performed an initial inspection to understand the dataset's structure and content.

1. Data Inspection:

• Checked column names, data types, and row counts.
• Analyzed missing values and inconsistent data entries.

1. Data Cleaning:

• Removed irrelevant or incomplete rows/columns.
• Handled missing values by imputation or deletion, ensuring dataset consistency.

1. Feature Engineering:

• Created new columns for age_category and bmi_band:
• Age Categories: Segmented ages into groups such as young_adult, early_adult, mid_adult, etc.
• BMI Bands: Categorized BMI values into bands like Underweight, Normal, Obese Class I, etc.

1. Exploratory Data Analysis (EDA):

• Explored statistical relationships and trends using the following techniques:
• Statistical Analysis: Performed aggregation and correlation calculations.

• Visualizations: Created histograms, scatter plots, bar charts, and pie charts to illustrate findings.

1. Insights Extraction:

• Summarized insights derived from EDA, highlighting key relationships and patterns in the data.

Key Insights and Visualizations

1. BMI and Smoking Patterns:

• Calculated the percentage of smokers with BMI above 30.
• Visualized the distribution of smokers and non-smokers using histograms with KDE overlays.

1. Regional Insurance Charges:

• Analyzed the average charges for each region and visualized comparisons using bar charts.
• Investigated regional proportions of individuals based on specific criteria (e.g., BMI ranges, smoking habits).

1. Price Categorization by Children:

• Calculated the average charges based on the number of children per family.
• Displayed results using a bar chart.

1. Scatter Plot Analysis:

• Examined the impact of BMI on charges for smokers vs. non-smokers using a scatter plot.

1. Demographic Patterns:

• Visualized age group distribution using a bar chart.
• Analyzed BMI bands to understand population health distribution and visualized counts with a bar chart.

Skills Acquired

• Data cleaning and preprocessing using Python.
• Advanced feature engineering techniques.
• Exploratory data analysis with Pandas, Matplotlib, and Seaborn.
• Creating insightful visualizations to communicate data findings effectively.

Conclusion

This project highlights the importance of analyzing key factors, such as BMI, smoking habits, and demographics, on insurance charges. The insights derived are valuable for insurance companies to enhance pricing strategies and customer segmentation. Future work could involve using predictive models to forecast insurance charges based on historical data.