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20bca_practicals_mvs

Thirsd Semester practicals for 20BCA students, sjc, paper: MVS

assignment-04-simple-linear-regression-2

Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

bioactivity--drug-discovery

This bioinformatics project predicts bioactivity using data preprocessing, descriptor calculation, and machine learning models. It involves loading bioactivity data, calculating Lipinski descriptors, normalizing IC50 values, and using PaDEL-Descriptor for fingerprint calculations. Finally, it builds a Random Forest Regressor model to evaluate

breast-cancer-analysis

This code performs an analysis on the Breast Cancer Wisconsin dataset, including generating a pair plot and calculating descriptive statistics. It also performs cluster analysis on the data using Agglomerative Clustering and plots the resulting clusters.

buan-4310-project-problem-1

car-price-hypothesis

This code performs a one-sample t-test on the enginesize column of the CarPrice_Assignment.csv dataset to determine whether the mean value of this column is significantly different from 120.

car-price-prediction

clothing_similarity_search

The goal of this project is to create a machine learning model capable of receiving text describing a clothing item and returning a ranked list of links to similar items from different websites. Your solution must be a function deployed on Google Cloud that accepts a text string and returns JSON responses with ranked suggestions.

currencyapp

customer-reviews-sentiment-analysis

This code is a demonstration of how to perform sentiment analysis on customer review data using the Natural Language Tool Kit (nltk) library.

eda-iris

This code uses pandas and various visualizations to perform EDA on a dataset of iris flower data. Summary statistics, scatter plots, pair plots, a heatmap, and a box plot are used to explore the data and identify patterns and relationships between features.

fifa-2018-factor-analysis

This code performs factor analysis on a dataset containing soccer statistics from the 2018 FIFA World Cup.

hadoop

hierarchical-clustering-of-iris-data

This code performs hierarchical clustering on the Iris dataset, using the Agglomerative Clustering method to group the data into 3 clusters. It also generates a dendrogram plot to help determine the optimal number of clusters and plots the resulting clusters using different colors.

hierarchical-clustering-wholesale-customer

This code performs hierarchical clustering on a dataset of wholesale customer data. It uses the Agglomerative Clustering method to group the data into 2 clusters and visualizes the clusters using a scatter plot. The code also generates a dendrogram plot to help determine the optimal number of clusters

text-analysis

The Python script performs text processing on a set of URLs by extracting article text using BeautifulSoup and creating a directory of text files. It then loads stop word files and NLTK stop words, and creates a positive and negative words dictionary. The script also defines a function to count syllables in a given text.

user-purchase-prediction

This code is a machine learning model that uses logistic regression to predict whether a user will purchase a product or not, based on the user's age and estimated salary. It applies feature scaling to the input data and evaluates the model's performance using a confusion matrix and an accuracy score.

wine-eda-clustering

xg-boost-telecom

This code is for applying the XGBoost algorithm on a telecom dataset. The dataset is first loaded and some basic information about it is printed, such as the number of null values and the data types of each column. The input variables and target variable are then separated, and the input variables are one-hot encoded.