Wasim Hassan Shah's repositories
Airbnb_Data_Analysis
Conducted comprehensive analysis of Airbnb data utilizing advanced Python data manipulation techniques with Pandas and visualized key findings using Matplotlib. Derived actionable insights and recommendations to drive incremental business generation and enhance decision-making processes for optimizing Airbnb listings and user experiences
Anamoly_Detection_Model
Detecting Defected Manufactured Semi-Conductors using Isolation Forest and Local Outlier Factor Algorithms
Banking_system
Banking_System_Project using C++ and OOP
Monthly_Retail_Sales_Forecasting_fpp
fpp: Data for "Forecasting: principles and practice"
Bayesian_Statistics
R packages for Bayesian models
Combined_Cycle_Power_Prediction
To develop a predictive model for full-load output power (PE) of Combined Cycle Power Plant usinf Linear Regression Random Forest and XG Boost Algorithms
Credit-Risk-Modelling
Random Forest and SVM Radial is used R
Depression_Detection_Model
This repository applies Deep Learning techniques for depression detection in text, using LSTM, GRU, BiLSTM, BERT models, and a baseline FFNN. It also includes data visualizations, autoencoder semantics, KMeans clustering, and detailed performance comparisons.
Drugs-Recommendation-Model
Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.
Electrical_cost_prediction_home_Neural_Network
Home Electrical bill and it's Forecasting using Neural Network
Exploratory-Data-Analysis-On-Electric-Vehicle-Population
The goal of this project is to analyse and visualise the Electric Vehicle Population dataset from Kaggle using Python (Matplotlib, seaborn and plotly).
Fuel_Consumption_Model
Designed a predictive vehicle fuel consumption model to estimate consumption based on various vehicle parameters (sklearn)
IMDB-Movie-Rating-Predictive-Model
tried to predict movie ration using KNN, CART, Random Forest.
Leaf-Disease-Detection-CNN
About This work presents a simple CNN-based technique for early detection of tomato leaf disease using 22948 images from the New Plant Diseases Dataset. The proposed model has a training accuracy of 97.6% and a test accuracy of 93.6%, making it an efficient and effective tool for farmers to identify and mitigate losses due to tomato leaf disease.
Price_Used_Cars
Price Prediction of Used Cars
Purchasing-Intention-Model
classification or clustering and predict the intention of the Online Customers Purchasing Intention
Python
Practice
Quick_Sort
C++ Program for QuickSort
R
R important pkgs
Smart-Grid-Theft-Detection-in-AMI-Model-
a Smart Grid Theft Detection Model for identifying electricity theft in AMI (Advanced Metering Infrastructure) based on customers' consumption patterns using K_means Clustering and ANN (Sklearn, TensorFlow)
Smart_Grid_Fraud_Detection
The aim of this project is the Detection of Fraud in Smart Grids using Machine Learning Techniques, such as: Logistic Regression SVM Decision Trees Random Forests
Smart_Grid_Stability_Model
Predicting Smart Grid Stability with Deep Learning, ANN
Twitter-Sentiment-Analysis
Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization