Delete my_portfolio

my_portfolio/README.md

@@ -1,24 +0,0 @@
-# WELCOME TO MY PORTFOLIO
-#DATA SCIENCE PROJECTS SECTION
-
-#PROJECT 1:  [FAIRLY USED CAR PREDICTION PLATFORM OVERVIEW](https://github.com/sadiksmart0/Used-Car-ML)
-
-* 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.
-* Platform can predict take direct input from a user or take a csv file and run predictions on them.
-* Used postgres to save user predictions and user can query past prediction from the database.
-* Airflow to schedule data ingestion and prediction jobs.
-* Used Grafana to monitor model and MLFlow for retraining
-
-https://github.com/sadiksmart0/my_portfolio/blob/main/videos/Fairly-used.mp4
-
-#Project 2: Music Emotion Recognition and Recommendatation.
-
-* 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.
-
-
-#Project 3: Movie Recommendations Overview

my_portfolio/app.py

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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('C:/Users/A.M. MUKTAR/my_portfolio/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("C:/Users/A.M. MUKTAR/my_portfolio/Images/pipeline.png")
-    image2 = Image.open("C:/Users/A.M. MUKTAR/my_portfolio/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("""
-    - 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.image("https://static.streamlit.io/examples/dice.jpg")
-    st.markdown("[Project CODE](https://github.com/sadiksmart0/DataVisualizationProject)")
-  
-
-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("""
-    - 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.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("""
-    - 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.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("""
-    - 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.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')
-st.subheader('Dataiku')

my_portfolio/videos/Fairly-used.mp4

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-oid sha256:8a8af4fe6ef00f765a7f1496652d94ee307edef547ea560811daeae7e9b57a71
-size 27906155

my_portfolio/videos/movie-recommender.mp4

@@ -1,3 +0,0 @@
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-oid sha256:ed8dfb4881b256ce9dea8690195ed66c2a1d56cd40ebdcd00cee4ceb8871e04b
-size 19207311

my_portfolio/videos/music-mood.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:422eac24929a3a900f1d4354b7987846dc0948625e5efe2f30e4f97bdee4fef0
-size 19784159

my_portfolio/videos/requirements.txt

@@ -1 +0,0 @@
-streamlit==1.19.0