import streamlit as st from PIL import Image st.title(' _Welcome to my Projects Portfolio_ ') with st.sidebar: image = Image.open("Images/IMG-20220413-WA0005.jpg") st.image(image) st.subheader("Interest") st.markdown(""" - Football - Reading - Cycling """) col1, col2 = st.columns(2) with col1: st.write("Name: Abubakar Muhammed Muktar") st.write("Status: Masters Student, Data Science & Analytics") st.write("School: EPITA") with col2: st.write("Strength: Serial learning, knowing I can always improve.") st.write("Favourite Quote: In God we trust, Everyone else bring data!") st.header("Data Science and Engineering Project Section") with st.expander("PROJECT 1: Fairly Used Car Prediction Platform"): st.subheader('Fairly Used Car Prediction Platform') st.write("This project is ...") st.markdown(""" - Created a price estimation model for fairly used car using Linear Regression - Developed a web platform Using Streamlit and deployed the model as a service\n - Platform can predict take direct input from a user or take a csv file and run predictions on them\n - Used postgres to save user predictions and user can query past prediction from the database\n - Airflow to schedule data ingestion and prediction jobs\n - Used Grafana to monitor model and MLFlow for retraining. """) st.markdown("[Project CODE](https://github.com/sadiksmart0/Used-Car-ML)") video_file = open('videos/Fairly-used.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes) with st.expander("PROJECT 2: Music Emotion Recognition and Recommendatation."): st.subheader('Music Emotion Recognition and Recommendatation') st.write("This project is ...") st.markdown(""" - Collaborated and developed a state-of-the-art deep learning model using BERT and gensims Doc2Vec for recognizing song emotion and give recommendations based on that given lyrics, song title and artist name. - Deployed the model on Heroku and serve the it using FastApi. - Develop and deployed the app on streamlit. - Presented the work as part of our masters thesis. """) st.markdown("[Project CODE](https://github.com/anthonybassaf/music-mood-recognition)") video_file = open('videos/music-mood.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes) with st.expander("PROJECT 3: Brain Tumor Segmentation"): st.subheader('Brain Tumor Segmentation') st.write("This project is ...") st.markdown(""" - Created a deep learning model based on the U-net architecture to segment brain tumor images. - Used tensorflow in the implementation. - Engineered the data into desired format. - Evaluated model performance based on Dice loss """) st.markdown("[Project CODE](https://github.com/sadiksmart0/Image-seg)") video_file = open('videos/Fairly-used.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes) with st.expander("PROJECT 4: Movie Recommendatation system."): st.subheader('Movie Recommendatation system.') st.write("This project is ...") st.markdown(""" - Implemented a movie recommendation system for using the cosine similarity, users and movie rating. - Scrape the web for movie posters and details using BeautifulSoup - Built a streamlit app for the recommendation plaform - Employed TF-IDF for tokenization. """) st.markdown("[Project CODE](https://github.com/sadiksmart0/Movie-Recommender)") video_file = open('videos/movie-recommender.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes) with st.expander("PROJECT 5: End-to-End Data Engineering Project using Kaggle YouTube Trending Dataset"): st.subheader('Movie Recommendatation system.') st.write("This project intends to manage, simplify, and analyze structured and semi-structured YouTube video data based on video categories and trending metrics in a secure manner.") st.markdown(""" - Implement the data pipeline completely using AWS cloud. - Data Lake to hold raw ingested data using Amazon S3 - Used AWS Lambda to preprocess the data to a parquet. - Data Warehouse to hold cleansed data in Amazon S3 - Catalogue the data using AWS Glue. - Used Athena to query the data. - Used IAM to create rule and policies to allow access accross these tools - Used QuickSight to run analysis on our final data - Used cloudwatch to monitor all of the processes for easy tracking. """) image1 = Image.open("Images/pipeline.png") image2 = Image.open("Images/analytics.png") st.image(image1) st.image(image2) st.header("Data Analysis Project Section") st.subheader('Pandas') with st.expander("PROJECT 1: Analysis of Ligue 1 From 2010-2021"): st.subheader('Analysis of Ligue 1 From 2010-2021') st.write("This project intends to ...") st.markdown(""" - Analyzed 12 season for Ligue 1 - Used plotly express and dash for visualization. - Pandas to load and run analysis. - Streamlit for the web app. """) st.markdown("[Project CODE](https://github.com/sadiksmart0/DataVisualizationProject)") video_file = open('videos/ligue1.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes) with st.expander("PROJECT 1: Analysis of Google play Apps"): st.subheader('Analysis of Google play Apps') st.write("This project intends to ...") st.markdown(""" - Analyzed google play apps. - Utilized matplotlib for visualization. - Utilized jupyter notebook for the analysis. """) # # st.image("https://static.streamlit.io/examples/dice.jpg") st.markdown("[Project CODE](https://github.com/sadiksmart0/Android-App-Market/blob/main/Android%20App%20Market.ipynb)") with st.expander("PROJECT 1: Analysis of Netflix movies"): st.subheader('Analysis of Netflix movies') st.write("This project intends to ...") st.markdown(""" - Analyzed Netflix movies dataset. - Utilized matplotlib for visualization. - Utilized jupyter notebook for the analysis. """) # # st.image("https://static.streamlit.io/examples/dice.jpg") st.markdown("[Project CODE](https://github.com/sadiksmart0/Netflix-Movies/blob/main/Netflix-Movies.ipynb)") with st.expander("PROJECT 1: Analysis of Nobel Prize Winners"): st.subheader('Analysis of Nobel Prize Winners') st.write("This project intends to ...") st.markdown(""" - Analyzed Nobel prize winners from the dataset. - Utilized matplotlib for visualization. - Utilized jupyter notebook for the analysis. """) # # st.image("https://static.streamlit.io/examples/dice.jpg") st.markdown("[Project CODE](https://github.com/sadiksmart0/Nobel-Prize/blob/main/Nobel_Prize.ipynb)") st.subheader('Tableau') with st.expander("PROJECT 1: Analysis of Nobel Prize Winners"): st.subheader('Analysis of Nobel Prize Winners') st.markdown("[Project Dashboard](https://prod-uk-a.online.tableau.com/#/site/esieadataviz/views/NobelPrizeWinners/Dashboard1?:iid=1)") with st.expander("PROJECT 2: E-commerce data"): st.subheader('Analysis of E-commerce data') st.markdown("[Project Dashboard](https://prod-uk-a.online.tableau.com/#/site/esieadataviz/views/E-commerce/SalesDashboard?:iid=1)") st.subheader('Dataiku')