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.gitattributes

@@ -35,3 +35,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 my_portfolio/videos/Fairly-used.mp4 filter=lfs diff=lfs merge=lfs -text
 my_portfolio/videos/movie-recommender.mp4 filter=lfs diff=lfs merge=lfs -text
 my_portfolio/videos/music-mood.mp4 filter=lfs diff=lfs merge=lfs -text
+videos/Fairly-used.mp4 filter=lfs diff=lfs merge=lfs -text
+videos/movie-recommender.mp4 filter=lfs diff=lfs merge=lfs -text
+videos/music-mood.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,167 @@
+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')

videos/Fairly-used.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a8af4fe6ef00f765a7f1496652d94ee307edef547ea560811daeae7e9b57a71
+size 27906155

videos/movie-recommender.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed8dfb4881b256ce9dea8690195ed66c2a1d56cd40ebdcd00cee4ceb8871e04b
+size 19207311

videos/music-mood.mp4

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

videos/requirements.txt

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