vaitybharati/vaitybharati

Config files for my GitHub profile.

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vaitybharati/Assignment-13-KNN-K-Nearest-Neighbors-Zoo-

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vaitybharati/Assignment-13-KNN-K-Nearest-Neighbors-Glass-

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vaitybharati/Assignment-12-Naives-Bayes-Classifier-Salary-

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vaitybharati/Assignment-18-Time-Series-Analysis-Forecasting-Airlines-Passengers-

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vaitybharati/Assignment-18-Time-Series-Analysis-Forecasting-CocaCola-Prices-

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vaitybharati/Inferential-Statistics

Inferential Statistics using Confidence Interval

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vaitybharati/Tableau-_Basics5

Tableau-_Basics Tutorial 4

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vaitybharati/XGBM-and-LGBM

XGBM-and-LGBM

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vaitybharati/Web-scraping Fork of vaisakhnambiar/Web-scraping

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vaitybharati/Visualization-Mat_Seaborn

Visualization using Matplotlib and Seaborn

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vaitybharati/Tableau_Basics6

Tableau_Basics Tutorial 6

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vaitybharati/Tableau_Basics9

Tableau_Basics Tutorial 9

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vaitybharati/Tableau_Basics7

Tableau_Basics Tutorial 7

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vaitybharati/t_SNE

t_SNE - Training and testing model

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vaitybharati/Text-Processing_Feature-Extraction

Feature Extraction, bigrams and trigrams, TFidf vectorizer, Generate wordcloud

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vaitybharati/Tableau_Basics8

Tableau_Basics Tutorial 8

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vaitybharati/Tableau-Basics

Tableau basics tutorial

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vaitybharati/Tableau_Basics2

Tableau_Basics2 tutorial

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vaitybharati/Tableau-_Basics4

Tableau-_Basics Tutorial 4

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vaitybharati/Tableau-_Basics3

Tableau-_Basics3 Tutorial

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vaitybharati/Simple-linear-Reg-1

Simple-linear-Reg-1

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vaitybharati/Survival-Analytics

Applying KaplanMeierFitter model on Time and Events

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vaitybharati/SVM

SVM

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vaitybharati/Stack-Overflow-1-Python-Concatenate

Concatenate two time columns into one using pandas

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vaitybharati/Sample-Datasets

Sample datasets for practice (Vega datasets)

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vaitybharati/R_tutorial1

R_tutorial1 - Basic Arthematic

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vaitybharati/scikit-learn-tips Fork of justmarkham/scikit-learn-tips

:robot::zap: scikit-learn tips

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vaitybharati/R_basics_calc-2

R code 2

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vaitybharati/R_Basics_calc1

R code 1a

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vaitybharati/R_basics-homework-earthquake

R_basics- Earth Quake data

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vaitybharati/R_basics-homework-5_sept

R_basics - Visualizing Air Quality data

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vaitybharati/R_basics

R_basics

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vaitybharati/P35.-Unsupervised-ML---Recommendation-System-Data-Mining-Movies-

Unsupervised-ML-Recommendation-System-Data-Mining-Movies. Recommend movies based on the ratings: Sort by User IDs, number of unique users in the dataset, number of unique movies in the dataset, Impute those NaNs with 0 values, Calculating Cosine Similarity between Users on array data, Store the results in a dataframe format, Set the index and column names to user ids, Slicing first 5 rows and first 5 columns, Nullifying diagonal values, Most Similar Users, extract the movies which userId 6 & 168 have watched.

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vaitybharati/R_basics-homework

R_basics Functions

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vaitybharati/Reviews_Classification_Naive_Bayes

Data Cleaning, N-gram, WordCloud, Applying naive bayes for classification, Using TFIDF

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vaitybharati/P26.-Supervised-ML---Multiple-Linear-Regression---Cars-dataset

Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.

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vaitybharati/P36.-Supervised-ML---Decision-Tree---C5.0-Entropy-Iris-Flower-

Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

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vaitybharati/P32.-Unsupervised-ML---Association-Rules-Data-Mining-Titanic-

Unsupervised-ML---Association-Rules-Data-Mining-Titanic. Data Preprocessing: As the data is categorical format, we are using One Hot Encoding to convert into numerical format. Apriori Algorithm: frequent item sets & association rules. A leverage value of 0 indicates independence. Range will be [-1 1]. A high conviction value means that the consequent is highly depending on the antecedent and range [0 inf]. Lift Ratio > 1 is a good influential rule in selecting the associated transactions.

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vaitybharati/Probabilty-calc-2

Probability Calculation in Python

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vaitybharati/Ridge_Lasso_ElasticNet

Model Building and Testing using Ridge, Lasso and ElasticNet Methods

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vaitybharati/P28.-Supervised-ML---Logistic-Regression---Appointing-Attorney-or-not

Supervised-ML---Logistic-Regression---Appointing-Attorney-or-not. EDA, Model Building, Model Predictions, Testing Model Accuracy, ROC Curve plotting and finding AUC value.

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vaitybharati/Recommendation-Engine

Recommendation-Engine

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vaitybharati/R3

R3 - Joins and Appling Functions in R

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vaitybharati/R-code-2

R-code-2

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vaitybharati/R1

R Basics Tutorial-1

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vaitybharati/R2

R2 - Decision Making statements in R

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vaitybharati/R-Basics2

R-Basics2 homework

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vaitybharati/P31.-Unsupervised-ML---DBSCAN-Clustering-Wholesale-Customers-

Unsupervised-ML---DBSCAN-Clustering-Wholesale-Customers. Import Libraries, Import Dataset, Normalize heterogenous numerical data using standard scalar fit transform to dataset, DBSCAN Clustering, Noisy samples are given the label -1, Adding clusters to dataset.

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vaitybharati/P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)

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vaitybharati/P18.-Hypothesis-Testing-2-Sample-2-Tail-Test-Drugs-and-Placebos-

Hypothesis-Testing-2-Sample-2-Tail-Test-Drugs-and-Placebos. Note: This python code states both 2-sample 1-tail and 2-sample 2-tail codes. Treatment group mean is Mu1 Contrl group mean is Mu2 2-sample 2-tail ttest Assume Null Hypothesis Ho as Mu1 = Mu2 Thus Alternate Hypothesis Ha as Mu1 ≠ Mu2.

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vaitybharati/R-code-1a

R-code-1a

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vaitybharati/Probability-Calc

Probability Calculations for Normal distribution

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vaitybharati/P34.-Unsupervised-ML---t-SNE-Data-Mining-Cancer-

Unsupervised-ML-t-SNE-Data-Mining-Cancer. Import Libraries, Import Dataset, Convert data to array format, Separate array into input and output components, TSNE implementation, Cluster Visualization

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vaitybharati/P21.-Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers-

Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers. Assume Null Hypothesis as Ho: Independence of categorical variables (Athlete and Smoking not related). Thus Alternate Hypothesis as Ha: Dependence of categorical variables (Athlete and Smoking is somewhat/significantly related). As (p_value = 0.00038) < (α = 0.05); Reject Null Hypothesis i.e. Dependence among categorical variables Thus Athlete and Smoking is somewhat/significantly related.

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vaitybharati/PCA

PCA

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vaitybharati/P25.-Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data

Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.

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vaitybharati/Pandas

Pandas Tutorial

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vaitybharati/P33.-Unsupervised-ML---PCA-Data-Mining-Univ-

Unsupervised-ML---PCA-Data-Mining-Univ. Import Dataset, Converting data to numpy array, Normalizing the numerical data, Applying PCA Fit Transform to dataset, PCA Components matrix or covariance Matrix, Variance of each PCA, Final Dataframe, Visualization of PCAs, Eigen vector and eigen values for a given matrix.

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vaitybharati/P04.-Matplotlib-Visualization

Plotting two different categories- box plot, barplot, histogram. Plotting single category- Pie chart, bar chart. Different Plots- Scatter Plot, Histogram, Box Plot, Violin Plot

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vaitybharati/P29.-Unsupervised-ML---Hierarchical-Clustering-Univ.-

Unsupervised-ML---Hierarchical-Clustering-University Data. Import libraries, Import dataset, Create Normalized data frame (considering only the numerical part of data), Create dendrograms, Create Clusters, Plot Clusters.

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vaitybharati/P17.-Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak-

Hypothesis-Testing-1-Sample-1-Tail-Test-Salmonella-Outbreak. 1-sample 1-tail ttest. Assume Null Hypothesis Ho as Mean Salmonella <= 0.3. Thus Alternate Hypothesis Ha as Mean Salmonella > 0.3. As No direct code for 1-sample 1-tail ttest available with unknown SD and arrays of means. Hence we find probability using 1-sample 2-tail ttest and divide it by 2 to get 1-tail ttest.

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vaitybharati/P22.-Hypothesis-Testing-Chi2-Test-Human-Gender-and-Choice-of-Pets-

Hypothesis-Testing-Chi2-Test-Human-Gender-and-Choice-of-Pets. Assume Null Hypothesis as Ho: Human Gender and choice of pets is independent and not related. Thus Alternate Hypothesis as Ha : Human Gender and choice of pets is dependent and related. As (p_valu=0.1031) > (α = 0.05); Accept Null Hypothesis i.e Independence among categorical variables. Thus, there is no relation between Human Gender and Choice of Pets.

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vaitybharati/P24.-Supervised-ML---Simple-Linear-Regression---Newspaper-data

Supervised-ML---Simple-Linear-Regression---Newspaper-data. EDA and Visualization, Correlation Analysis, Model Building, Model Testing, Model predictions.

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vaitybharati/P20.-Hypothesis-Testing-Anova-Test---Iris-Flower-dataset

Hypothesis Testing Anova Test - Iris Flower dataset. Anova ftest statistics: Analysis of varaince between more than 2 samples or columns. Assume Null Hypothesis Ho as No Varaince: All samples population means are same. Thus Alternate Hypothesis Ha as It has Variance: Atleast one population mean is different. As (p_value = 0) < (α = 0.05); Reject Null Hypothesis i.e. Atleast one population mean is different Thus there is variance in more than 2 samples.

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vaitybharati/P23.-EDA-1

EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

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vaitybharati/P16.-Hypothesis-Testing-1S2T---Call-Center-Process

Hypothesis Testing 1S2T - Call Center Process. Sample Parameters: n=50, df=50-1=49, Mean1=4, SD1=3 1-sample 2-tail ttest Assume Null Hypothesis Ho as Mean1 = 4 Thus, Alternate Hypothesis Ha as Mean1 ≠ 4

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vaitybharati/P19.-Hypothesis-Testing-2-Proportion-T-test-Students-Jobs-in-2-States-

Hypothesis-Testing-2-Proportion-T-test-Students-Jobs-in-2-States. Assume Null Hypothesis as Ho is p1-p2 = 0 i.e. p1 ≠ p2. Thus Alternate Hypthesis as Ha is p1 = p2. Explanation of bernoulli Binomial RV: np.random.binomial(n=1,p,size) Suppose you perform an experiment with two possible outcomes: either success or failure. Success happens with probability p, while failure happens with probability 1-p. A random variable that takes value 1 in case of success and 0 in case of failure is called a Bernoulli random variable. Here, n = 1, Because you need to check whether it is success or failure one time (Placement or not-placement) (1 trial) p = probability of success size = number of times you will check this (Ex: for 247 students each one time = 247) Explanation of Binomial RV: np.random.binomial(n=1,p,size) (Incase of not a Bernoulli RV, n = number of trials) For egs: check how many times you will get six if you roll a dice 10 times n=10, P=1/6 and size = repetition of experiment 'dice rolled 10 times', say repeated 18 times, then size=18. As (p_value=0.7255) > (α = 0.05); Accept Null Hypothesis i.e. p1 ≠ p2 There is significant differnce in population proportions of state1 and state2 who report that they have been placed immediately after education.

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vaitybharati/P15.-Hypothesis-Testing-1S1T---Super-Market-Loyality-Program

Hypothesis-Testing 1S1T-Super-Market-Loyality-Program. Population Parameters: Mean=120 Sample Parameters: n=80, Mean=130, SD=40, df=80-1=79

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vaitybharati/P11.-Normal-Distribution-of-Stocks

To understand Normal Distribution and its application. Daily returns of stocks traded in BSE (Bombay Stock Exchange). To understand risk and returns associated with various stocks before investing in them. BEML and GLAXO Stocks study.

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vaitybharati/P14.-Confidence-Interval-for-Stocks

Find confidence intervals for Beml and Glaxo stocks. Confidence Interval Estimate

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vaitybharati/P12.-C.I.E-using-z-values-Confidence-Interval-Estimate-

credit card launch example sample mean: 1990 sample SD: 2833 Pop SD: 2500 Pop mean: ? n=140 Q: Construct 95% confidence interval for mean card balance and interpret it

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vaitybharati/P10.-Probability-Calc-2

Suppose GMAT scores can be reasonably modeled using a normal distribution with mean=711 and SD = 29. What is P(X<=680) What is P(697<=X<=740)

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vaitybharati/P13.-C.I.E-using-t-values-Confidence-Interval-Estimate-

credit card launch example sample mean: 1990 sample SD: 2833 Pop mean: ? n=140 (In cases, where pop SD is not known, use t-values and practically in all problems prefer t over z) Q: Construct 95% confidence interval for mean card balance and interpret it

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vaitybharati/P01.-Pandas-1

Understanding Pandas, Importing datasets, Deriving Attributes, Performing Statistics

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vaitybharati/P07.-Chebyshev-s-practice

Chebyshev's Theorem 3/4th or 75% of observations lie 2 Standard deviations of mean i.e. mean+2SD and mean-2SD

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vaitybharati/P06.-Seaborn-Visualization-Titanic

Seaborn Visualization on Titanic Dataset Visual exploration of different features on No. of people survived or otherwise Visualization using FacetGrid function, Lambda function and criterion function Visualization of subplots

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vaitybharati/P05.-Seaborn-Visualization

Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot

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vaitybharati/P09.-Probability-Calc-1

Find the probability that a normally distributed random variable has a mean of 60 and a standard deviation of 10 and we want to find the probability of x is less than 70.

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vaitybharati/Named_Entity_Recognition_Emotion_Mining

Named Entity Recognition , Emotion Mining in Python

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vaitybharati/P08.-Box-Plot-Practice

Box Plot - using dataframe in pandas Inserting Minor and Major gridlines Deriving LQ, UQ, IQR, Upper Whisker and Lower Whisker length

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vaitybharati/Normal-Distribution

Normal-Distribution

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vaitybharati/Neural-Network_Back-Propagation

Model building and testing using NN Back Propagation

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vaitybharati/P02.-Pandas-2

Understanding Pandas, Groupby Function, Filtering Function

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vaitybharati/NN_Hyperparameter-Tuning

Tuning of Hyperparameters :- Batch Size and Epochs. Tuning of Hyperparameters:- Learning rate and Drop out rate. Tuning of Hyperparameters:- Activation Function and Kernel Initializer. Tuning of Hyperparameter :-Number of Neurons in activation layer. Training model with optimum values of Hyperparameters.

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vaitybharati/P03.-Pandas-3

Understanding Pandas, Visualization using Matplotlib, Plotting subplots

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vaitybharati/P00.-Sample-Datasets

Sample Datasets Database for Data Science ML algorithms practice

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vaitybharati/mysql_null-commands

mysql_null-commands

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vaitybharati/Mysql-Students-table

Mysql-Students-table

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vaitybharati/Mysql-date-time

Mysql-date-time

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vaitybharati/Mysql-Data-Manipulation

Mysql-Data-Manipulation

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vaitybharati/Mysql-practice-tables

Mysql-practice-tables

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vaitybharati/mysql-create-drop-rename

mysql-create-drop-rename

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vaitybharati/ml Fork of KxSystems/ml

Machine-learning toolkit

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vaitybharati/Mysql-Alter-commands

Mysql-Alter-commands

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vaitybharati/Matplotlip

MatPlotlib Python codes

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vaitybharati/Multi-Linear-Reg

Multi-Linear-Reg

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vaitybharati/Model-Validation-Methods

Model-Validation-Methods

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vaitybharati/Logistic-Regression

Logistic-Regression

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vaitybharati/KNN

K Nearest Neighbours in Python

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