Divya Parashar's repositories
911_Call_Analysis
Analyzing the traffic of 911 calls
Astar_vs_dijkstra
Comparing the performance of A* and Dijkstra algorithm
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.
Constraint_Satisfaction
Applying Constraint Satisfaction on different problems
Covid_19_Data_Analysis
Data Visualization using Seaborn and Matplotlib
Exercise_Matplotlib
Some initial exercises on Matplotlib
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.
Introduction_to_Python
Basic programs to get started with Python.
Matplotlib_Data_Analysis
Data analysis through Matplotlib library
NumPy_Data_Analysis_
Matrix operations through Numpy
Pandas_Data_Analysis
Manipulating numerical tables and time series through Pandas
Running_BarPlot_Graph
Running Graph of City Vs Population.
suicide_analysis
Data analysis through visualization to find different causes for suicide.