Parashar7

911_Call_Analysis

Analyzing the traffic of 911 calls

Astar_vs_dijkstra

Comparing the performance of A* and Dijkstra algorithm

BarPlot

Running BarPlot Graph of City Vs Population

Bayes-Classifier

Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.

Ensemble_Learning

Demonstrating Ensemble Learning using Bagging and Random Forest algorithm

LSTM_Feature_Selection

Using LSTM for prediction of stock prices on different features used for training the LSTM model.

ML_Classification_Algorithms_Comparison

Comparative performance analysis of Naive Bayes, SVM(Support Vector Machine), and Random Forest(Bagging) algorithms for Spam Filtering.

Performance-Evaluation-of-LSTM-and-RNN-in-Stock-Price-Prediction-of-NASDAQ-Index

This article uses 2 important models for the predictions and comparisons. These are Long Short-Term Memory and Recurrent Neural Network measures. The result of LSTM and RNN are compared to check the most optimal model for stock forecasting. For this, various metrics and visualization are considered using different independent variables for both the models. We are going to estimate this using different plot criteria, RMSE value, and R2 score of different number of independent variables for both LSTM and RNN.

Regression

Regression models target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.

Support_Vector_Machine

SVMs are used for Classification as well as Regression problems. However, it is primarily used for Classification problems. #%% md # The Technique (Support Vector Machine) Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.

Parashar7/911_Call_Analysis

Analyzing the traffic of 911 calls

Parashar7/Astar_vs_dijkstra

Comparing the performance of A* and Dijkstra algorithm

Parashar7/BarPlot

Running BarPlot Graph of City Vs Population

Parashar7/Bayes-Classifier

Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.

Parashar7/Ensemble_Learning

Demonstrating Ensemble Learning using Bagging and Random Forest algorithm

Parashar7/LSTM_Feature_Selection

Using LSTM for prediction of stock prices on different features used for training the LSTM model.

Parashar7/ML_Classification_Algorithms_Comparison

Comparative performance analysis of Naive Bayes, SVM(Support Vector Machine), and Random Forest(Bagging) algorithms for Spam Filtering.

Parashar7/Performance-Evaluation-of-LSTM-and-RNN-in-Stock-Price-Prediction-of-NASDAQ-Index

This article uses 2 important models for the predictions and comparisons. These are Long Short-Term Memory and Recurrent Neural Network measures. The result of LSTM and RNN are compared to check the most optimal model for stock forecasting. For this, various metrics and visualization are considered using different independent variables for both the models. We are going to estimate this using different plot criteria, RMSE value, and R2 score of different number of independent variables for both LSTM and RNN.

Parashar7/Regression

Regression models target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.

Parashar7/Support_Vector_Machine

SVMs are used for Classification as well as Regression problems. However, it is primarily used for Classification problems. #%% md # The Technique (Support Vector Machine) Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.

Parashar7/Constraint_Satisfaction

Applying Constraint Satisfaction on different problems

Parashar7/Covid_19_Data_Analysis

Data Visualization using Seaborn and Matplotlib

Parashar7/Exercise_Matplotlib

Some initial exercises on Matplotlib

Parashar7/Fake_News_Identification

Parashar7/hill_climbing_algorithm

Hill Climbing is a heuristic search used for mathematical optimization problems in the field of Artificial Intelligence. Given a large set of inputs and a good heuristic function, it tries to find a sufficiently good solution to the problem. This solution may not be the global optimal maximum.

Parashar7/Introduction_to_Python

Basic programs to get started with Python.

Parashar7/KNN

One of the simplest Machine Learning algorithm based on Supervised Machine Learning approach.

Parashar7/Matplotlib_Data_Analysis

Data analysis through Matplotlib library

Parashar7/NumPy_Data_Analysis_

Matrix operations through Numpy

Parashar7/Pandas_Data_Analysis

Manipulating numerical tables and time series through Pandas

Parashar7/Running_BarPlot_Graph

Running Graph of City Vs Population.

Parashar7/suicide_analysis

Data analysis through visualization to find different causes for suicide.