Restaurant Recommendation using collaborative Filtering
We are extracting real time data from Yelp
Dataset.csv is extracted
Collaborative filtering is used for recommendation results
Python Demo
Students Result prediction using machine learning
Student result prediction using machine learning with algorithms Decision Tree and Random forest algorithm. The feature selection technique is used to select top important feature from dataset. The selected features are given for learning to the algorithm. The performance of the models are compared.
Real Estate Price Prediction using Lasso algorithm and Logistics Regression
Python Demo
Cricket Score Prediction using Decision Tree and Random Forest Algorithm – Python
Algorithm used for Cricket score prediction are decision tree and random forest and accuracy between these two techniques arrived.
Python Demo
Improving Safety on Drivers for Vehicular Ad Hoc Networks
Need for individualizing vehicular communications in order to improve safety for a highway scenario. Adapting a vehicular ad hoc network to both its individual driver’s characteristic and traffic conditions enables it to transmit in a smart manner to other vehicles. This radical improvement is now possible due to the progress that is being made in vehicular ad hoc networks (VANET). Packet success probability is derived for a chain of vehicles by taking multi-user interference, path loss, and fading into account. Then, by considering the delay constraints and types of potential collisions, we approximate the optimal channel access probabilities.
NS2 Demo
Implementation Details:
Heart Disease Prediction using K-Means clustering and Logistics Regression
1. We are taken dataset data.csv
2. Input data.csv is split into three cluster
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
