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.
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:
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
The below list of available python projects on Machine Learning, Deep Learning, AI, OpenCV, Text Editior and Web applications. Software requirements are python programming, Anaconda , etc.
- Lyrics Scrapper from website
- Phishing website detection
- Pneumonia detection using deep learning
- Customer Spending classification using K means clustering
- Titanic data clustering on survived data.
- Recipe Recommendation system using K means clustering
- Character detection from images using OCR
- Crude Oil Prediction using SVR & Linear Regression
- Face Recognition based Criminal Identification system
- Language Translator and converting voice to text
- Face detection based attendance system
- Automatic Land mark classification using Deep Learning
- Automatic Brand Logo detection using Deep learning
- Fake News Detection Using Naïve Bayes Classifier
Python Text Editor
- Number plate recognition using opencv
- Emotion based music player
- Detection of brand logos from given images
- Color recognition using neural networks for determining the ripeness of a banana
- Vision Sentiment Analysis using googleapi cloud
- Sentiment Analysis
- Classification Of IRIS Flowers Using Scipy Library In Machine Learning
- Visualize Machine Learning Data Using Pandas
- A Framework for Analysis of Road Accidents
- Wal-Mart Sales Prediction
- Bigmart Sales Prediction
- IIT Paper Analysis
- Disease Prediction using machine learning
- Heart Disease Prediction
- Custom Digit Recognition
- Rain fall prediction using svm, Artificial neural network, liner regression models.
- Self Driving Car Simulation using AI
- Crop prediction using linear regression
- Automatic question and answer generation using NLP
- Vehicle counting for traffic management
- Python Image processing using opencv.
- Pedestrian detection
- Custom Digit Recognization
- Driver Drowsiness detection using opencv.
- Iris species predictor flask web app
- Medical data analysis using machine learning using flask webapp
- Youtube spam detection using flaskwebapp.
- Named Entity Recognition and sentiment analysis using flask webapp.
- Text summarizer and comparison using flaskwebapp.
- Gender classification based on name.
- Image encryption compression and decompression and decryption
- Data encryption using aes,des algorithms
- Toll gate management system
- Image stegnography using lsb algorithm
- Prediction house worth using machine learning
- Securing data using hybrid cryptography in cloud
- Evaluating Employee Attrition
- Improving security for login using two factor( password and QR code) method.
- Heart Disease Diagnosis based on symptoms
- Automation of test evaluation for objective and subjective tests
- Phishing website detection
- License Detection Using QR Code
- E Plastic
- Student Help desk
- E Waste
- Online Shopping
- E farming
- Visualizing Machine Learning Using Pandas
- Detecting Pneunomia using Machine Learning
- Two factor authentication using QR code APP for user login
- House Worth Prediction based on machine learning
- Water Marking Image
- Analysing and Detecting Money Laundering
A social forum for villagers to be held so that they can spread the problem, improve it and anyone in the world can see and answer. 3D images of wells, data visualization, data analysis. Previously, If any problem occurs in village, people has to go and ask higher authorities and also there is no interaction between people and higher officials. So they can only solve their own problems because of no communication between Rural and Urban people. Everything is digitized, People can easily share their problems all over the places, So here in this paper we created a platform that any one (village people, officers, common people)can login into the system and do the operations, This project also having a special feature called Prediction, Farmers can easily predict the agriculture fluctuations based on the previous data, This feature helps to people when they will do the agriculture, this project also involves farms, wells, houses. Anyone can interact with any person and post their problems. This application is implemented using Python, Django, database like dbsqlite.
INDEX TERMS : Village management, Farmers, Problems.
Previously, If any problem occurs in village, people has to go and ask higher authorities and also there is no interaction between people and higher officials. So they can only solve their own problems because of no communication between Rural and Urban people.
Admin module: Such administrative help you change FirstSearch to serve the needs of the user. This module provides information that acts as the backbone of the remainder of the system. The security issue is dealt with through the module discussed the rights of users.
Volunteer module: Volunteer modules allow you to help people in the village who provide services such as medical care, roads, and transport, etc. Model volunteer work is crucial to understanding criminal needs and providing good support.
Reporter module: Reporter module allows for the unattended processing of alarm signals and the reporters are employed to report news.
Farmers Module: Farmer module can add their Problems to add in the Website
Problems Module: If any Problems occurred in village entered in to this Application.
OS : Windows
Python IDE : Python 2.7.x and above
Setup tools and pip to be installed for 3.6.x and above
RAM : 4GB and Higher
Processor : Intel i3 and above
Hard Disk : 500GB Minimum
Maintaining data uniqueness is one of the important features for many areas like in colleges and universities Plagiarism check and data uniqueness is one of the main criteria for preparing a paper of paper publishing. In order to maintain plagiarism free content, there is a need for effective methods wherein the existing method used should rewrite entire content manually if there are many pages of content to be written then it takes a lot of time which is time taking process.
With the advancement of machine learning and artificial intelligence, we can develop an application that can automate process of content rewriting. In this project, we are developing an application in python named article rewriter or plagiarism remover in python which will rewrite entire given content in a short time. In this project, Natural language processing is used in which text summarizer libraries are used. In this project, we also compare the output of different text summarizer algorithms.
In the existing system in order to remove plagiarism for the content manual process was involved in which the user should understand each meaning of the sentence and rewrite the entire content with its own words. Which is time taking process.
In this project, NLP is used for understanding input text or data from URL and then summarize text and rewrite entire content in a short time by using a text summarizer algorithm.
Operating system: Windows 7 or 10.
Tool :Anaconda ( Jupiter )
Software :Python 3.5
Dependencies : numpy
Libraries: pandas, keras, scipy, sklearn,NLP
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from the medical background of different people ( prime Indians dataset from UCI repository). This data set consists of information of the user whose age, sex type of symptoms related to diabetes. Design a testing and training set and predict what are the chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
To analyze data by considering exiting the user’s data set and predict what are the chances of diabetes in the coming five years. Information is shown in the form of different graphs.
Data analysis is playing an important part in analyzing datasets and predicting what are situations in the coming years. This analysis can give the option for departments and organizations to take steps in dealing with these problems. In this project prediction of diabetes in the coming years is considered as the main problem.
There was no chance of prediction in existing studies it was just by manual analysis based on existing data but analyzing large amounts of datasets is not considered.
Data analysis and machine learning libraries and algorithms are used for prediction on diabetes and information is shown in detail in the form of different types of graphs (histogram, density plots, box and whisker plots, and correlation matrix plots.
SOFTWARE & HARDWARE REQUIREMENT:
OS: Windows 7 or above
Processor: I3 or above
Programming language: python 3.6
Distribution tool: Anaconda.
RAM: 4 GB
Hard Disk: 160 GB