Pinned Repositories
Association-Rules
Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots
Basic-Statistics_1
Basic-Statistics_2
Clustering
Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. 2 .Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
Decision-Tree_2
Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Decision_Trees
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
Download-YouTube-videos-in-Python
Forecasting-_1
Forecast the CocaCola prices data set. Prepare a document for each model explaining
Forecasting_2
Forecast the Airlines Passengers data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.
House-Prices
SadikAnsari15's Repositories
SadikAnsari15/Association-Rules
Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots
SadikAnsari15/Clustering
Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. 2 .Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
SadikAnsari15/Decision-Tree_2
Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
SadikAnsari15/Decision_Trees
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
SadikAnsari15/Download-YouTube-videos-in-Python
SadikAnsari15/Forecasting-_1
Forecast the CocaCola prices data set. Prepare a document for each model explaining
SadikAnsari15/Forecasting_2
Forecast the Airlines Passengers data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.
SadikAnsari15/House-Prices
SadikAnsari15/KNN
1.Prepare a model for glass classification using KNN. 2.Implement a KNN model to classify the animals in to categorie
SadikAnsari15/KNN-Classifier-
iris dataset
SadikAnsari15/Labs
SadikAnsari15/Linkedin_code_ML_py
Machine_Learning_code_python
SadikAnsari15/Logistic_Regression
SadikAnsari15/Multi_LInear_Regression
1 .Prepare a prediction model for profit of 50_startups data.Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. 2. Consider only the below columns and prepare a prediction model for predicting Price.
SadikAnsari15/Naive_Bayes
1) Prepare a classification model using Naive Bayes for salary data
SadikAnsari15/Neural-Networks_1
1.predicting turbine energy yield (TEY) using ambient variables as features.
SadikAnsari15/Neural-Networks_2
PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
SadikAnsari15/NumPy
NumPy, "Numerical Python," is a fundamental Python library for numerical computing. It provides support for working with large, multi-dimensional arrays along with an extensive collection of mathematical functions to operate on these arrays. NumPy is a crucial library in the Python ecosystem for data analysis, machine learning, scientific research.
SadikAnsari15/PCA
Perform Principal component analysis
SadikAnsari15/python-code-LMS-ML
SadikAnsari15/python_basic
python_basic_operations
SadikAnsari15/Random-Forests_1
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A Random Forest can be built with target variable Sales (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
SadikAnsari15/Random-Forests_2
Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
SadikAnsari15/Recommendation_system
Build a recommender system by using cosine simillarties score.
SadikAnsari15/Scrape-Movies-Name-Release-Year
To scrape movie names and release years from a web page,use Python libraries such as requests to fetch the HTML content and BeautifulSoup to parse and extract information.
SadikAnsari15/Sentimental-Analysis-on-customer-reviews
This Project is made up on Natural Language Processing To Perform Sentimental Analysis On Amazon’s Product Reviews and classified Positive and Negative reviews which are given by the customers
SadikAnsari15/Simple_Linear_Regression
1) Delivery_time -> Predict delivery time using sorting time 2) Salary_hike -> Build a prediction model for Salary_hike
SadikAnsari15/Support-Vector-Machines
1. classify the Size_Categorie using SVM. 2. Prepare a classification model using SVM for salary data
SadikAnsari15/Text-Mining
Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv) (2).1) Extract reviews of any product from ecommerce website like amazon 2) Perform emotion mining
SadikAnsari15/Titanic