Stock Prediction
1. Data Collection Phase:

a. We have collected real time data from nse website.

b. Any stock with NSE symbol can be given in the textbox “Enter Symbol” Ex. SBIN, HDFC, TCS, INFY

c. Press Submit button to extract real time data from 2000 to present date

d. Feature collected are Date, Symbol, Series, Prev close, Open, High, Low, Last, Close, VWAP, Volume, Turnover

e. Also shown price trend in graph

2. Prediction Phase
——————–

a. We have applied Kmeans Clustering, we have created 3 clusters namely, Cluster0, Cluster1, Cluster2

b. Centroid plot is done for Three cluster

c. Random Forest and Decision Tree algorithm is applied in Clusters for prediction.

d. Predicted value and Real (original) values are ploted.

e. Accuracy and Error values are arrived

3. Forecast Phase
——————
a. Five days forecast using Decision Tree and Random forest is done.

Python Demo

Python Demo

The emerging of need of domestic robots in real world applications has raised enormous need for instinctive and interaction among human and computer interaction (HCI). In a few conditions where humans can’t contact hardware, the hand motion recognition framework more suitable. Hand gesture recognition is used on controlling robots, portable controllers, or application in smart home. Gesture recognition is mainly applicable for video conferencing, sign language recognition, distance learning and in some forensic identification. Based on fingers’ angles relative to the wrist, a finger angle prediction algorithm and a template matching metric are proposed. All possible gesture types of the captured image are first predicted, and then evaluated and compared to the template image to achieve the classification. Unlike other template matching methods relying highly on large training set, this scheme possesses high flexibility since it requires only one image as the template, and can classify gestures formed by different combinations of fingers.

Python Demo

Python demo

Location, e.g., Countries, states, cities, and point-of-interests, are central to news, emergency events, and people’s daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In proposed system, an overall picture of location prediction on Twitter is offered. Specifically, the prediction of user home locations, tweet locations, and mentioned locations is considered. By summarizing Twitter network, tweet content, and tweet context as potential inputs, it is structurally highlighted how the problems depend on these inputs

Location Prediction in Twitter using Machine learning Techniques
—————————————————————————
Implementation Details:
——————————————–

1. Data Collection:
——————————–
1. Collect Live tweet based on location (we used three locations such as Chennai, Mumbai, Kerala)
2. Live tweets are collected as json file

2. Data Pre-processing:
—————————————
Tweet text is pre-processed with following steps

1. Remove extra characters and special characters
2. Convert all words to Capitalize first letter (needed to apply geography package)
3. If tweet location & user location is null, then remove the tweet
4. If tweet location is null, then apply home location as tweet location
5. If no location is mentioned in tweet then remove the tweet
6. Apply Geodata on tweet_text to identify location
7. Apply Lableencoder to convert text (location from tweet_text) to integer value”Lvalue” (e.g Chennai=1, Mumbai=2, Kerala=3)
6. Write the pre-processed dataset as csv file with following features
a. Tweet ID
b. Name
c. Screen name
d. Tweet Text
e. Home location
f. Tweet location
g. Mentioned location
h. Lvalue (location value)

3. Split Training Set & test Set
4. Apply Naive Bayes, SVM and Decision Tree algorithm
5. Calculate and compare accuracy and error values for all above algorithm

Diagnosis of liver diseases using machine learning

Liver Diseases account for over 2.4% of Indian deaths per annum. Liver disease is also difficult to diagnose in the early stages owing to subtle symptoms. Often the symptoms become apparent when it is too late. This paper aims to improve diagnosis of liver diseases by exploring 2 methods of identification patient parameters and genome expression. The paper also discusses the computational algorithms that can be used in the aforementioned methodology and lists demerits. It proposes methods to improve the efficiency of these algorithms.

Python Demo

Real Estate Price prediction Regression, Lasso, SVM, Decision Tree

Python Demo

Cricket match result prediction using four ML algorithms

Decision Tree, SVM, Naive Bayes, Logistics Regression

Python demo

Rainfall Prediction Implementation Details:
We are taking dataset

X Train & Test is from ID to Oct-Dec (NOT ANNUAL Column)
Y Train & Test is the Annual column

We are taking dataset and Analysing dataset & plotted all graphs.

Using Train set of X & Y

we are applying ML algorithm Lasso and Decision Tree

For X testset, we are arriving results and stored as resultLasso & resultDecisionTree

Python Demo

k means++ Cluster algorithm for Heart Disease prediction

Implementation Details:

Heart Disease Prediction using K-Means and K-means++ clustering and Logistics Regression

1. We are taken dataset data.csv

2. Input data.csv is split into three cluster by K-means algorithm taking centroid automatically. Whereas k-means++ arrives centroid with distance

Cluster 0, Cluster1, Cluster2

3. Every cluster data is taken for getting trainset and test set

Trainset contains 14 columns, whereas testset contains 13 column

4. Every cluster testset is predicted for heart disease

Accuracy is arrived
5. Logistics regression is performed and accuracy arrived

Python demo