Students Marks Prediction Using Linear Regression

Abstract:

Analyzing and prediction of academic performance is important for any education institutions. Predicting student performance can help teachers to take steps in developing strategy for improving performance at early stages. With the advancement of machine learning supervised and unsupervised techniques developing these kinds of applications are helping teachers to analyze students in better way compare to existing methods. In this student marks prediction using Linear regression project students’ academic performance is prediction considering input as previous students marks and predict next subject marks and accuracy of the model is calculated.

Problem statement:

Analyzing and prediction of marks for students was done based on guess and students’ personal marks details are not considered for academic evaluation.

Objective:

Machine learning based data mining techniques are used to automate process of student performance prediction using linear regression technique.

Existing system:

  • Researches has done work on Grading systems which final examination marks are used for giving grades for students and evaluation of each student is done.
  • Association rule mining and apriori algorithms are used for classifying students based on their marks

Disadvantages:

  • Most of these methods work on data mining techniques which are based on after completing data.
  • Early stage evaluation is not possible in these methods.

Proposed system:

  • Students marks of other subjects are taken as input for evaluation students’ performance. Data set is pre-processed and features and labels are extracted from dataset then dataset is split in to test and train sets then linear regression is applied to dataset for prediction.

Advantages:

  • Before final marks of all subjects are evaluated prediction can be performed.
  • Using machine learning process automation of marks prediction can be done.

SOFTWARE REQUIREMENTS:

  • Operating system                  : Windows XP/7/10
  • Coding Language            :            python 
  • Development environment : anaconda, Jupiter
  • Dataset                         : students marks dataset
  • IDE :           Jupiter notebook

COVID-19 Data Analysis And Cases Prediction Using CNN

ABSTRACT:

Corona virus ( COVID-19 ) is creating panic all over the world with fast growing cases. There are various datasets available which provides information of world-wide effected information. Covid has affected all counties with large number of cases with variation of numbers under death, survived, effected. In this project we are using data set which has county wise details of cases with various combined features and labels. Covid data analysis and case prediction project provide solution for data analysis of various counties on various time and data factors and creating model for survival and death cases and prediction cases in future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for next few months are done using this project.  

PROBLEM STATEMENT:

      With the increase of COVID 19 cases all over the world daily predictions and analysis is required for effective control of pandemic all over the world

OBJECTIVE:

    By collecting data from Kaggle and new York dataset data preprocessing is performed and data analysis is performed on dataset and machine learning model is generated for future prediction of cases.

EXISTING SYSTEM:

  • Prediction was performed on COVID 19 cases based on different machine learning techniques which are based on x ray data set collected from COVID 19 patients.
  • Disease prediction from x ray images is done using deep learning techniques.

Disadvantages:

  • Data set used for predicting disease is different compare to one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

Using data set pre-processing is performed on the collected data set and various steps for deep learning model is performed and prediction of cases is done then data analysis is done on various factors.

Advantages:

  • Data analysis and prediction is performed on textual data
  • Deep learning models are generated for predicting future cases.
  • Data analysis is performed for various factors.

SOFTWARE REQUIREMENTS

  • Operating system                   :           Windows XP/7/10
  • Coding Language              :           python
  • Development Kit                  :        anaconda 
  • Programming language: Python
  • IDE                                               :           Anaconda prompt

Crop Yield Prediction using KNN classification

ABSTRACT:

Agriculture is considered as import field all over the world where there are many challenges in solving problems in the process of estimating crops based on the conditions. This has become a challenge for developing countries.  Using latest technologies many companies are using IOT based services and Mechanical technology to reduce manual work. These methods are mostly useful in the case on reducing manual work but not in prediction process. In this project crop yield prediction using Machine learning latest ML technology and KNN classification algorithm is used for prediction crop yield based on soil and temperature factors.  Dataset is prepared with various soil conditions as features and labels for predicting type of each label is related to certain crop. In prediction process user can give input as soil features and result will be type of crop suitable for specific conditions and application also helps in suggesting best crops with yield for hector.

PROBLEM STATEMENT:

  • In our country large amount of population are depending on agriculture though government is taking financial steps to help farmers still they are facing problems due to lack of data analysis and prediction on crops.

OBJECTIVE:

  • Our objective is to develop an application using machine learning for predicting which crop to be used based on soil condition using k nearest neighbor classification.

Existing system:

          Image based analysis was one of the methods which was previously used for detection land type and then analysis was done.

Disadvantages:

         Process is based on image analysis results are not accurate as in this method soil conditions are not considered.

       Image processing is a time taking process.

Proposed system:

        Machine learning is the latest technology which python programming language gives advantage in using various algorithms for crop yield prediction based on the input data set. In this process KNN classification algorithm is used for prediction. In this project testing training is performed on given text dataset which includes soil and temperature conditions as features and type of crop as labels.

Advantages:

        Crop yield prediction is performed based on textual dataset and any user can check type of crop best suits for conditions and get crop suggestions. 

 

System Requirement:

  • Operating system         :           Windows XP/7/10
  • Coding Language :           Html, JavaScript, 
  • Development Kit :        Flask Framework
  • Programming language: Python
  • IDE :           Anaconda prompt

Stock Market Analysis Python Project Report

Stock Market Analysis and prediction is a project for technical analysis, visualization, and estimation using Google Financial data. Seeing data from the market, especially some general and other software columns. Pandas used to take stock of the information, looked at different aspects of it, and finally looked at it in some way to assess the risk of a stock based on its recent performance history. Competing with the Monte Carlo method in anticipation of future prices.

OVERVIEW

Stock exchange analysis is only intended for the analysis of stock company data for various organizations. Using this method of data analysis, any organization can easily extract relevant information.

AIM OF THE PROJECT

The main goal of my project is to analyze the data of all the institutions in which form we need.

PROBLEMS FOR THE ANALYSIS

Share financial data with quandl for the following companies:

  • Apple
  • Amazon
  • Microsoft
  • Google


Perform basic data analysis

  • Get last year’s data
  • Check Apple values
  • Indicate the final price
  • The stock market has seen a rate hike
  • Gather all the company data together for the final price


Make daily return analyzes and show the relationship between the different stocks

  • Percentage change plan for Apple product
  • Find a shared website for Apple and Google
  • Use PairPlot to show the relationship between everything

Perform risk analysis


CONCLUSION AND FUTURE SCOPE


We evaluated two basic measurements of the analysis and found no conclusive evidence about their estimated value.
These predictions are also very long-lasting and will see a year in the future. Suggestions on this scale are not the main project time. Instead, we will focus on predicting daily market trends. Because of these problems, we avoided basic analysis.

Performing risk analysis Results:

Download the attached  Stock Market Analysis python Project Report

 

Python Projects

The below list of available python projects on Machine Learning, Deep Learning, AI, OpenCV, Text Editor, and Web applications. Software requirements are python programming, Anaconda, etc.

PYTHON 2020 TITLES:

NETWORK SECURITY ANAMOLY DETECTION

 

1.     Anomaly detection in Network Traffic Using Unsupervised Machine Learning Approach.

2.     Outlier detection in indoor localization and Internet of Things (IoT) using machine learning.

3.     Machine Learning-based anomaly detection for IoT Network: (Anomaly detection in IoT Network)

4.     A Study on Machine Learning Based Anomaly Detection Approaches in Wireless Sensor Network

5.     BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset.

6.     Anomaly Detection in Smart Grids using Machine Learning Techniques

7.     Fraud Detection in Credit Card Data using Unsupervised Machine Learning Based Scheme.

8.     CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection.

9.     Detect malicious SQL queries via both a blacklist and whitelist approach

10.  PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

11.  A Cyberbullying Detection in live chatting Using Machine Learning Techniques.

12.  Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism

POWER PREDICTION

  1. Solar Energy Forecast Using Machine Learning

DISEASE DETECTION

  1. An Efficient IoT-Based Platform for Remote Real-Time Cardiac Activity Monitoring.
  2. Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms.
  3. Alzheimer Disease Prediction using Machine Learning Algorithms.
  4. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
  5. LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification
  6. Breast cancer detection and algorithm comparison Decision tree and SVM classifiers
  7. Automated Prediction of Non-Alcoholic Fatty Liver Disease using Machine Learning Algorithms
  8. HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System
  9. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

STOCK MARKET:

  1. Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

AGRICULTURE:

  1. Crop Yield Prediction based on Indian Agriculture using Machine Learning

EDUCATION:

  1. A Machine learning Model for Prediction of Graduate Admissions
  2. Students Performance Prediction in Online Courses Using Machine Learning Algorithms
  3. Predicting admission acceptance and rejection of Universities based on Education data.

VIRTUAL REALITY

  1. A Virtual Trial Room using Pose Estimation and Homograph.
  2. Virtual Ornaments trial system Using AI

COLLISION DETECTION

  1. Smart Edge Healthcare Data Sharing System
  2. Path Planning with Improved Artificial Potential Field Method Based on Decision Tree

SOCIAL NETWORKING

  1. Twitter Bot Detection with Reduced Feature Set
  2. Detection of Depression-Related Posts in Reddit Social Media Forum
  3. Fake News Detection: An Ensemble Learning Approach
  4. Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network
  5. Defensive Modeling of Fake News Through Online Social Networks
  6. FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
  7. Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

DEEP LEARNING AND IMAGE PROCESSING:

  1. Plant Leaf Disease Detection Using CNN
  2. Predicting Fashion Products type from Image Using sequential Model
  3. Predicting Covid-19 from X-ray Images using VGG16 CNN Algorithm
  4. Rice Leaf Disease detection using CNN Model
  5. A survey on detection and classification of rice plant diseases
  6. Confidence measure guided single image de-raining study image processing
  7. Underwater image enhancement techniques for benchmark dataset and beyond study and analysis.
  8. An Efficient IoT-Based Platform for Remote Real-Time Cardiac Activity Monitoring
  9. Soil Classification using Deep Learning
  10. Robust Face-Name Graph Matching for Movie Character Identification

MACHINE LEARNING: 

  1. Amazon Data Rating Review Analysis and Predicting rating from review using Linear SVM Algorithm.
  2. Automatic Disaster Message Classification using Linear Algorithm.
  3. Advance Detection of Machine Failure in Automated Industries Using ML Algorithms.
  4. A Decision Model for Human Resource Allocation in Project Management of Software Development
  5. Detection of Epileptic Seizure Event and Onset Using EEG using Machine Learning

DATA ANALYSIS: 

  1. Analysis of Online Shopping order History and predict Future Orders using SARIMA Model.
  2. Analyzing Telecom customers Data for Improving Services.
  3. Analyzing satellite images and clustering areas using K-means
  4. Time Series Analysis of Sales Data
  5. Amazon Data Rating Review Analysis and Predicting rating from review using Linear SVM Algorithm.
  6. COVID 19 Data Analysis and Cases Count Prediction using CNN.
  7. Analyzing Data to suggest the best stream for higher studies.
  8. Keyword trend analysis using Google API

ARTIFICIAL INTELLIGENCE: 

  1. An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network
  2. Fire and Gun Violence based Anomaly Detection System Using Deep Neural Networks.
  3. Voice-based Gender Detection and Online shopping product category classification.
  4. Detecting Active Features in given Image using Scale-invariant feature transform algorithm.
  5. Chatbot application based on User Input using LSTM (hospital, colleges’)
  6. Hand Gesture detection using AI
  7. Human Gender and Emotion detection from live voice recordings using AI.
  8. Social Distancing Detection with Deep Learning Model
  9. Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences

MACHINE LEARNING: (SIMPLE WITH ALGORITHM) 

  1. Laon data analysis and prediction using K means
  2. Classifying and predicting Brest cancer using the Decision Tree Algorithm.
  3. Liner Regression-based student Marks prediction
  4. User Ads spending Prediction based on salary using Logistic Regression Algorithm

Latest Topics:

  1. Lyrics Scrapper from website
  2. Phishing website detection  
  3. Pneumonia detection using deep learning
  4. Customer Spending classification using K means clustering
  5. Titanic data clustering on survived data.
  6. Recipe Recommendation system using K means clustering
  7. Character detection from images using OCR
  8. Crude Oil Prediction using SVR & Linear Regression
  9. Face Recognition based Criminal Identification system
  10. Language Translator and converting voice to text
  11. Face detection based attendance system
  12. Automatic Land mark classification using Deep Learning
  13. Automatic Brand Logo detection using Deep learning
  14. Fake News Detection Using Naïve Bayes Classifier

Python Text Editor

  1. Number plate recognition using opencv
  2. Emotion based music player
  3. Detection of brand logos from given images
  4. Color recognition using neural networks for determining the ripeness of a banana

Machine Learning

  1. Vision Sentiment Analysis using googleapi cloud
  2. Sentiment Analysis
  3. Classification Of IRIS Flowers Using Scipy Library In Machine Learning
  4. Visualize Machine Learning Data Using Pandas
  5. A Framework for Analysis of Road Accidents
  6. Wal-Mart Sales Prediction
  7. Bigmart Sales Prediction
  8. IIT Paper Analysis
  9. Disease Prediction using machine learning
  10. Heart Disease Prediction
  11. Custom Digit Recognition
  12. Rain fall prediction using svm, Artificial neural network, liner regression models.
  13. Self Driving Car Simulation using AI
  14. Crop prediction using linear regression
  15. Automatic question and answer generation using NLP
  16. Vehicle counting for traffic management

  Opencv:

  1. Python Image processing using opencv.
  2. Pedestrian detection
  3. Custom Digit Recognization
  4. Driver Drowsiness detection using opencv.

Web Applications 

  1. Iris species predictor flask web app
  2. Medical data analysis using machine learning using flask webapp
  3. Youtube spam detection using flaskwebapp.
  4. Named Entity Recognition and sentiment analysis using flask webapp.
  5. Text summarizer and comparison using flaskwebapp.
  6. Gender classification based on name.
  7. Image encryption compression and decompression and decryption
  8. Data encryption using aes,des algorithms
  9. Toll gate management system
  10. Image stegnography using lsb algorithm
  11. Prediction house worth using machine learning
  12. Securing data using hybrid cryptography in cloud
  13. Evaluating Employee Attrition
  14. Improving security for login using two factor( password and QR code) method.
  15. Heart Disease Diagnosis based on symptoms
  16. Automation of test evaluation for objective and subjective tests
  17. Phishing website detection
  18. License Detection Using QR Code
  19. E Plastic
  20. Student Help desk
  21. E Waste
  22. Online Shopping
  23. E farming
  24. Visualizing Machine Learning Using Pandas
  25. Detecting Pneunomia using Machine Learning
  26. Two factor authentication using QR code APP for user login
  27. House Worth Prediction based on machine learning
  28. Water Marking Image
  29. Analysing and Detecting Money Laundering