KhizarSultan/Automated-Data-Analysis-System-using-Python

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Created about 5 years ago
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KhizarSultan/Automated-Data-Analysis-System-using-Python

KhizarSultan

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This project will solve the problem of educational systems who want to analysis of raw data and make decision on the basis of analysis. This project will generate all kind of graphs, charts , tables and calculate probabilities of raw data and save them in single and multiple file for the ease of user. Following tools and libraries are used in this project: 1)Pycharm, tkinter 2) numpy, scipy, pandas 3) matplotlib

CNIC_OCR_detection_using_DeepLearning

Mental_Health_Prediction_using_Machine_Learning

This project will solve the project to manually check weather a person have Mental Health disease or not.

Jupyter Notebook

Helmet_Detection_using_Deep_Learning

Custom tiny-yolo-v3 training using your own dataset and testing the results using the google colaboratory. Object detection using yolo algorithms and training your own model and obtaining the weights file using google colab platform.

Jupyter Notebook

Loan_Prediction_using_MachineLearning

this project will solve the problem of insurance companies who want to automate their system for loan will approve or not

Jupyter Notebook

Pacman-game_in_C-

This is pacman game in C++ developed by me

Turkey_Students_Evaluation_Unsupervised_Learning_Project

This is unsupervised learning problem in which i make clusters of the different groups based on their features and then convert it into supervised learning and apply PCA and got 99.5 % accuracy on test dataset

Jupyter Notebook

Data_Science_Assignments

This Repository contains all the assignments of data science, machine learning, deep learning and artificial intelligence

Jupyter Notebook

COVID_19_Analysis

# COVID_19_Analysis The Purpose of this project to understand the insight of COVID 19 Data using Python, This project is divided into two following parts (1) EDA (Exploratory Data Analysis) (2) Apply Competency Questions (i.e Prediction using Regression, jaccard similarity and Locality Sensitive Hashing) on the data to get insight from data. if you have any query or question, you can ask me without any hesitation at [email protected]

Jupyter Notebook

Churn_Classification_using_MachineLearning

This Project will solve the problem to check whether a customer is churn or not using Machine Learning. I did process the data, apply feature engineering and than ML Algorithm and got 85% accuracy on training data and 84% accuracy on test data. If you have any question regarding this notebook, please drop your email at [email protected]

Jupyter Notebook

Email_Spam_Classification_using_MachineLearning

Problem Statement: This tasks involves classifying a set of emails as spam or not spam. The data that you will be using to classify the data is available at https://www.cs.cmu.edu/~einat/EnronMeetings-XML.zip 1. Read and load data from all the files in the train folder 2. Formulate a dataset based on the content of the email. You can choose any criteria for classifying the content as spam or not spam but you need to justify your reasons. 3. Evaluate your model 4. Feed some testing data to it and classify is the email was spam or not #Solution: (1) i have read all the files in the train folder (2) Clean the files using NLP Techniques and create TF-IDF Matrix (3) Apply Kmeans clustering Algorithm to label the dataset i.e creat you clusters (1) Spam (2) Not Spam (4) Balance the dataset using SMOTE Algorithm (5) Apply GradientBosstingClassifier and Achieve 98% accuracy on validation data (6) Apply Model on the test data (7) save the ouput of test data into csv files #### NOTE: if you have any query or question regarding this task, please feel free to ask at the below mentioned email: [email protected]

MachineLearning_Algorithms

This repository will solve the problem of students who want to learn data science a-z with examples

Jupyter Notebook

Cat_Dog_Classification_using_DeepLearning

This is a deep learning project that identify either the picture of dog or cat using CNN

Iris_data_classification

This project will let you know the each step of iris flower classification using python

Jupyter Notebook

Sleep_Rat_classification_using_DeepLearning

It is a project of deep learning in which i classify the images of rat mind whether the rat is sleeping or wake using CNN.

Age_Detection_using_DeepLearning

This project will solve the problem to manually detect the age of a person or a baby using Deep Learning.

Resturant_Reviews_Analysis_using_NLP

This project will solve the problem of to manually check whether a review is positive or negative using NLP techniques.

Jupyter Notebook

Python

This repository will solve the problem of those students who want to learn python from a-z with examples.

object_detection_yolov3

Time_Series_Analysis_Using_Deep_Learning

This Project will predict the Gloabl Active Power using Deep Learning (LSTM)

Jupyter Notebook

Movie_Recommendation_System_using_Python

This project will recommend the movie according to User Taste (Collaborative Filtering), Similar Movies (Content Base Filtering) and Top rated movies.

Jupyter Notebook

IMDB_Movie_Review_Analysis_Using_NLP_MachineLearning

This Project will solve the problem to check whether a movie review is positive or negative using NLP and Machine Learning.

Jupyter Notebook

Python_Libraries_for_DataScience

These Libraries will let you know the complete understanding of Data Processing, Data Cleansing, Data Transformation and Data Analysis using python libraries

Jupyter Notebook

A-B-Test-of-Ecommerce-Website

For this project, I will be working to understand the results of an A/B test run by an e-commerce website. My goal is to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision

Jupyter Notebook

object_detection_using_Yolov3

YOLOv3 is the third object detection algorithm in YOLO (You Only Look Once) family. It improved the accuracy with many tricks and is more capable of detecting small objects.

Jupyter Notebook

Analysis_of_Big_Data_using_Spark

This project will analyse the big social data using KMeans Algorithm, we have used Spark and Mllib for this problem.

Jupyter Notebook

Plagiarism_checker_NLP

This project will solve the problem of teachers to check plagiarism of individual students. Teacher just upload the assignment folder of students and this system will automatcially find the plague in percetage among the student files using NLP techniques and generate a plague report of indivisual students.

Jupyter Notebook

Face_Recogntion_using_DeepLearning

This project will solve the problem to manually mark attendance of the students using Deep Learning. In this project, We did detect faces and then compute 128-d embedding vector of faces and then apply Random Forest Machine Learning Algorithm to train the model and got 97 % accuracy and finally recognize the faces in images and in video.

Black_Friday_Sale_Prediction_using_Machine_Learning

Predicting Prices for the products to be sold on Black Friday in US using Regression Analysis, Feature Engineering, Feature Selection, Feature Extraction and Data analysis - Data Visualizations.

Jupyter Notebook

Selecto-Analytical-Android-App

This app will solve the problem of businessmen who want to analyse all the daily sale and stock in the form of statistics. This app will show all kind of graphs and charts of daily sale, top sales, stock and many more. Following tools and libraries are used in this project (1) Android Studio (2) Firebase (3) Anychart library

KIPS-Academy-Management-system-using-Javascript-and-Php

This project will solve the problems of students and teachers using online portal for students where students can view their marks, result card, attendance and profile information. It is a web application using JavaScript and Php.

emotion_detection_using_deep_learning

# Emotion Detection - Kaggle Dataset :- https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data. - Change the number of classes according to you. - Do Experiment with different pre-trained models. - Enjoy Deep Learning.

Big-Mall-Sale-Prediction-using-machine_learning

This project will solve the problem of businessmen who want to predict the sell of product in coming year This project process the raw data of mart and apply machine learning regression algorithms to find the pattern in the data and make useful predictions Following tools and libraries are used in this project : 1) Jupyter Notebook 2) numpy, scipy, pandas 3) matplotlib and seaborn 4) scikit-learn

Jupyter Notebook

Learn_and_earn_app_using_Deep_Learning

The learn and earn app will have two actors teacher and student respectively. Our domain is Educational Purpose Students and teachers will register themselves according to their area of interest/skills of learning and teaching respectively. We will clean and transform the teacher and student data and then app will generate teacher’s CV and recommend best teachers using machine learning Algorithms. The App will provide a dashboard to teachers where all the students will be showed and teacher can deliver online live teaching sessions and videos and chats etc. Teacher will not manually check plagiarism and mark attendance, we will perform NLP (Natural Language Processing) to check the Plagiarism of students and then we also apply Computer Vision using Deep Learning to Automate Face Recognition Attendance and Emotion Detection for the ease of teacher.

mamogram_detection_using_ensemble_learning

his data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives.

Jupyter Notebook

Anime_Recommendation_using_Content_Based

Recommend Anime using Machine Learning.

Jupyter Notebook