Real Estate Price prediction Regression, Lasso, SVM, Decision Tree
Real Estate Price prediction Regression, Lasso, SVM, Decision Tree
Python Demo
Cricket match result prediction using Decision Tree, SVM, Naive Bayes, Logistics Regression
Cricket match result prediction using four ML algorithms
Decision Tree, SVM, Naive Bayes, Logistics Regression
Python demo
Three Dimensional Tagcloud visualization for tourism
Three Dimensional Tagcloud visualization for tourism
Metadata creation along with growth of social bookmarking emerged an approach named tagging. Often people look for location along with its route and detailed information about its surrounding. Mobile users may opt for current event that is taking place in the current location along with historical background of their surroundings and events that happen over time. They are provided with information on spatial context of location which harvests context information from freely available source and tag cloud visualization is created for this data. Firstly, Geo-referenced data which is close to selected point is gathered. Then gathered information is filtered based on the frequency of words from harvested data. Filtered data is then visualized as tag cloud in R shiny.
Implementation Details:
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User Query is passed to google api
Google results are retrieved and stored as data.csv file
data.csv file is visualized as tag cloud
R- Rshiny Demo
Rainfall Prediction using Lasso and Decision Tree alogrithm on Python
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
Students Result Prediction using Rshiny and MySQL
Students Result prediction using R and RShiny & MySQL
1. Importing dataset from csv file to MySQL database
2. Open Input form in RShiny and Give record input
3. This input is stored as test.csv file also
4. Naive Bayes Algorithm and Logistic regression are implemented to predict value
5. Accuracy and other plots are arrived
R Demo
Book recommendation using collaborative filtering- Java project
Discover how collaborative filtering can enhance book recommendations. Learn how this technique provides personalized reading suggestions based on user preferences and behaviors
k means++ Cluster algorithm for Heart Disease prediction
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
Personality Prediction in Tweets
PERSONALITY PREDICTION IN TWEETS
Implementation Details:
-1. Twitter data is collected for topic “apple” and stored as twitter.json file. The data will be added in the same file for execution of Twitterdata.py
2. Collected tweets from json file is extracted stored as tweet.csv
data extracted from each tweet are
tweet_id
tweet_time
tweet_author
tweet_author_id
tweet_language
tweet_text
polarity
tweet_sentiment
More 1000 tweets are collected
3. Naive Bayes and Logistics regression are applied, Plots are arrived
For the taken dataset, x-train and x-test and y-train & y-test are arrived.
from which te error is calculated for both technique
4. tweet.csv is taken as input and person.csv is extracted
This is the collection of unique autor and his total tweets, with positive and negative counts, and polarity aggregation. From which personality is arrived.
tweet_author
tweet_count
tweet_positive
tweet_negative
polarity
personality
Python Demo
Twitter sentiment analysis using five machine learning techniques
Twitter Sentiment Analysis using 5 Machine learning Techniques
Implementation Details:
1. Twitter data is collected for topic “apple” and stored as twitter.json file. The data will be added in the same file for execution of Twitterdata.py
2. Collected tweets from json file is extracted stored as tweet.csv
data extracted from each tweet are
tweet_id
tweet_time
tweet_author
tweet_author_id
tweet_language
tweet_text
polarity
tweet_sentiment
More 1000 tweets are collected
3. 5 machine learning techniques were applied
1.Naive Bayes
2.Logistics regression
3.SVM technique
4.Random forest and
5.K-means Clustering
Plots are arrived
For the taken dataset, x-train and x-test and y-train & y-test are arrived.
from which te error rate is calculated for all techniques
4. Accuracy for all techniques is arrived and plotted.
Python Demo