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| 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') | |