Portfolio

About Me

Data Analysis enthusiast with a strong academic background in Mathematics and Computer Science, experienced in Data Analysis, Machine Learning, and proficient in Advanced Excel, Python, SQL and Power BI. Desperately waiting to gain hands-on experience to combine tireless hunger for new skills with desire to exploit cutting-edge data science technology.

Education

Parul University, Gujarat [2020 – 2024]

Under Graduation: BTech, Computer Science Engineering with Specialization in Artificial Intelligence

Certifications

• Foundational Generative AI, iNeuron
• Prompt Engineering, AICTE
• Advanced Analytics, Swinfy Solutions Pvt Ltd

Technical Skills

• Operating System: Windows
• Data Visualization Tools: Power BI, Excel
• Programming Languages: Python, SQL, HTML, CSS
• Frameworks: Django, Streamlit
• Soft Skills: Self-motivated, Quick learner, Social, Resilient, Team Player, Public Speaker

Projects

- Streamlit Breast Cancer Predictor App

Objective: Build an app that predicts cancer by showing the visualization based on the given cell nuclei measurements.

Dataset Description:

• Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass, describing characteristics of the cell nuclei present in the image.

Attribute Information:

• ID number
• Diagnosis (M = malignant, B = benign)
• Ten real-valued features are computed for each cell nucleus:
• radius (mean of distances from center to points on the perimeter)
• texture (standard deviation of gray-scale values)
• perimeter
• area
• smoothness (local variation in radius lengths)
• compactness (perimeter^2 / area - 1.0)
• concavity (severity of concave portions of the contour)
• concave points (number of concave portions of the contour)
• symmetry
• fractal dimension (“coastline approximation” - 1)

The mean, standard error, and “worst” or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

• All feature values are recorded with four significant digits.
• Missing attribute values: none
• Class distribution: 357 benign, 212 malignant

Project Structure:

• It has 3 folders:
  • app:
    • main.py: contains the main function of the app
  • data:
    • data.csv: contains the dataset
  • model:
    • main.py: contains logistic regression model code to predict
    • model.pkl: pickle file of the model
    • scaler.pkl: pickle file of the scaler

Libraries Used:

• pandas: used to clean the dataset by removing unnecessary columns and transforming categorical variables into numerical ones.
• StandardScaler: used to preprocess numerical data before model training to ensure that different features have the same scale.
• train_test_split: used to split datasets into training and testing subsets.
• LogisticRegression: used to train a logistic regression model on labeled data where the target variable has two classes, predicting the probability of a sample belonging to a certain class.
• accuracy_score: used to compare the predicted labels to the true labels and return the accuracy ratio of correctly predicted samples to the total number of samples.
• pickle: used to save the machine learning model and scaler object into binary files to use later or in a different environment.
• streamlit: used to create the user interface, generate sliders, visualize graphs, and display predictions.
• plotly.graph_objects: used to create interactive plots and charts.
• numpy: used for numerical operations in data preparation.

Data Preparation:

• The dataset is cleaned by:
• Removing the unwanted columns for prediction: ‘id’, ‘Unnamed: 32’.
• Converting the output objects to integers.
• Scaling the values using StandardScaler.

Visualization:

• Radar charts are useful for displaying multiple quantitative variables.
• Plotly’s radar chart (go.ScatterPolar) is used for visualizing the mean, standard error, and worst values of various features related to breast cancer diagnosis.

App Snippet:

Results:

• The undeployed Streamlit app is built, which visualizes the input values from a slider using a radar chart to represent mean, standard error, and worst values for various features related to breast cancer diagnosis. The predictions are displayed on the right side of the app, along with the probabilities of being benign or malignant based on the logistic regression model training on the input.

Conclusion:

• The Streamlit framework made it easy to build a web application for machine learning by simplifying the creation of interactive and data-driven apps.

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- Streamlit Exploratory Data Analysis App

Objective: Create a one-line Exploratory Data Analysis (EDA) experience.

Working: This app analyzes the uploaded CSV files, providing in-depth insights into the dataset’s characteristics through exploratory data analysis techniques.

Features:

• Upload CSV: Allows users to upload their CSV files for analysis.
• Profiling Report: Generates an exploratory data analysis report using ydata_profiling.
• Example Dataset: Offers the option to explore an example dataset in the absence of an uploaded CSV file.

Libraries Used:

• numpy : used for various numerical operations, especially in generating example datasets or handling numerical data within the analysis.
• pandas : used to read and manipulate CSV files, organize data into DataFrames, and facilitates data exploration and presentation.
• streamlit : used as the primary framework for building the EDA web application, allowing easy integration of data visualizations, user inputs, and data analysis tools.
• ydata_profiling : Used to create comprehensive and interactive exploratory data analysis reports based on the uploaded or example datasets, providing detailed insights into the data’s characteristics.
• streamlit_pandas_profiling : Facilitates the direct embedding of Pandas Profiling reports generated by ydata_profiling into the Streamlit app, allowing users to visualize and interact with the analysis within the app interface.

App Snippet:

Result: Based on the uploaded CSV file or the example dataset, the EDA report is generated with the help of ydata-profiling and Streamlit on this app.

Conclusion: Streamlit framework made easy to build web application for machine learning by simplifying the creation of interactive and data-driven apps.

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- Exploratory Data Analysis (EDA) on Rural Telangana Illiteracy Rates

Objective: Analyse the number of illiterates in Telangana during the year 2014 and compare it with the current year(2023).

Data Source: The dataset is taken from Open Data Telangana

Dataset Description: The dataset provides information about the number of illiterates in the rural areas of Telangana State by gender to gram panchayat level. This data is according to the old districts during the perioid 2014.

Dashboard Snippet:

Insights Gained:

• During the period 2014, the top 3 highest number of illiterates according to the old districts were in Rural Telangana from Mahabubnagar with 14,75,524 members, Karimnagar with 10,58,837 members and Nalgonda with 10,45,305 members.
• And in Urban Telangana the top 3 highest number of illiterates were from Rangareddy with 5,10,184 members, Warangal with 1,85,698 members and Karimnagar with 1,67,189 members.
• The number of female illiterates was more than the male and transgender in Telangana.
• In Rural Telangana, Mahabubnagar district had 17% of illiterates of which maximum number of illiterates(6240) were from Makthal GP, and least(1) were in Rallacheruvu Thanda GP.
• In Urban Telangana, Rangareddy district had 34% of illiterates of which maximum number of illiterates(19126) were from Serilingampally Muncipality, and least(19) were in Ibrahimpatnam Muncipality.
• According to indiacensus.net, the top 3 highest illiterate districts of Telangana in the year 2023 are Jogulamba Gadwal(former Mahabubnagar), Narayanpet(former Mahabubnagar) and Nagarkurnool(former Mahabubnagar).

Result: Conducted in-depth analysis of the illiteracy rates in rural Telangana using data sourced from Open Data Telangana and analyzed the number of illiterates in 2014 and compared these statistics with the current year (2023) to gauge changes and trends in literacy rates over the years.

Conclusion: Even after 9 years, the district that was once known as Mahabubnagar has the highest percentage of illiterates in Telangana.

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- Web Scraping and Data Extraction from Amazon.in for Electronic Gadgets

Objective: Collect comprehensive data on electronic gadgets commonly used by software employees or students, such as laptops, tablets, smartphones, smartwatches, headphones, earphones, and earbuds, during the Diwali season to especially emphasize the offer deals.

Data Source: The data is scrapped from ‘amazon.in’ website.

Dataset Description: Our required data from the webpage:

• name: Title of the product
• brand: Brand of the product
• model_name: Model name of the product
• screen_size: Display size of the screen
• colour: Colour of the product
• cpu_model: CPU model of the product
• ram_memory_installed_size: Installed size of ram memory in the product
• operating_system: Operating System of the product
• mrp: Actual price of the product
• offer: Offer on the product
• number_of_purchase_in_last_month: Number of purchases of the product in last month
• number_of_ratings: Number of ratings received for the product
• rating: Overall rating of the product

Data Preperation: Required data was extracted from the webpage by finding the mentioned tags and if no such tag was found then that value is replaced with an empty string. Products having no title value were removed from the dataset and then saved as a csv file.

Libraries Used:

• requests : Used for sending HTTP requests to websites to fetch the HTML content of web pages
• BeautifulSoup (from bs4) : Used for parsing the HTML content obtained using the ‘requests’ library
• pandas : Used to create the DataFrame to organize and structure the scraped data
• numpy: Used for numerical operations, especially in handling numerical data.

Data Snippet

Result: Was successfully able to web scrape the amazon.in data once before the header i used got blocked or restricted.

Conclusion: Got to know that the success of web scrapping depends on various factors:

• Headers: Many websites require a valid user-agent string which allows/blocks/restricts the reuests.
• Website Policies: Some websites like amazon are having strict anti-scraping policies that are preventing or limiting scraping attempts.
• Website Changes: As we are relying on specific HTML tags to web scrape the data, sometimes our code might not be able to find the required HTML elements if the website changes its structure.

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