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https://github.com/sumeetgedam/data_analysis

Repository to track Data Analysis done on various datasets available online
https://github.com/sumeetgedam/data_analysis

algorithms arima-forecasting data-visualization pandas plotly-express plotly-graph-objects prophet-facebook python r2-score random-forest seaborn sklearn timeseries-forecasting trends visualization

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Repository to track Data Analysis done on various datasets available online

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README 

        
# Data_Analysis



Data Analysis on some famous datasets available online.



![](./Assets/dataset-cover.png)



## Content :clipboard:



1. [Kaggle](#kaggle)



    - [S&P 500 Analysis and Prediction](#sp-500-analysis-and-prediction)

    - [Market Analysis and EDA](#stock-market-analysis-and-eda)

    - [Market Analysis Basics](#stock-market-analysis)

    - [Time Series EDA on World War II](#time-series-eda-on-world-war-ii)

    - [Time Series Basics](#time-series-basics)

    - [Spotify Analysis](#spotify-analysis)

    - [Airbnb Analysis](#airbnb-analysis)

    - [E Commerce Analysis](#e-commerce-analysis)

    - [NBA Data Analysis](#nba-data-analysis)

    - [Premier League Data Analysis](#premier-league-data-analysis)

    - [Diamond Prices Data Analysis](#diamond-prices-data-anaysis)

    - [Titanic Survival Data Analysis](#titanic-data-anaysis)   



2. [Read](#reads)

    



## Kaggle



### S&P 500 Analysis and Prediction



  - Dataset

    - [S&P 500 stock data](https://www.kaggle.com/datasets/camnugent/sandp500/data?select=getSandP.py)



  - Notebook

    - [S&P 500 Analysis and Prediction](https://www.kaggle.com/code/sumeetgedam/s-p-500-analysis-and-prediction/)

  

  - Implementation Points

    - Explored dataset to understand data 

    - The analysis was focused on AMAZON stock data

    - Visualized the variation in Close, High, Low , Open using matplotlib

    - Forecasting was done using Prophet, Facebook's libirary for time series forecasting

    - Prophets plot and components showed the upward trend in Yearly as well as Monthly data of AMZN stocks

    - plotly's graph_objects was used for creating OHLC(Open High Low Close) , CandleStick graph

    - Analyzed American Airlines Stock for understanding seasonality trends

    - Plotted monthly forecasted data to see the seasonal trends in each year 



### Stock Market Analysis and EDA



  - Dataset

    - [Stock Market](https://www.kaggle.com/datasets/mahnazarjmand/stock-market)

  

  - NoteBook

    - [Stock Market Analysis and EDA](https://www.kaggle.com/code/sumeetgedam/stock-market-analysis-and-eda)



  - Implementation Points

    - Explored the Dataset to understand the structure

    - The NYSE Composite, NYA was the focus of analysis among othe available indices

    - Cleaned the data by dropping NA values and filled some with ffill method

    - Made *Date* attribute to datetime in pandas, to get price fluctuations over time

    - Removed Outliers and visualized Adjcent Close over time(Date)

    - used matplotlib, plotly to create pie charts and CandleStick on the refined data

    - Visualized correlation between attributes, 100 days simple moving average, 200 days simple moving average with matplotlib



### Stock Market Analysis



- Dataset

  - Yahoo Fianace by Python [yfinance](https://github.com/ranaroussi/yfinance)



- NoteBook

  - [Market Analysis Basics](https://www.kaggle.com/code/sumeetgedam/stock-market-analysis-basics)



- Implementation Points

  - Downloaded stock market data frmo Yahoo Finance website using yfinance

  - Explored and visualized time-series data using pandas, matplotlib, seaborn

  - Measured the correlation between stocks

  - Measured the risk to invest in them and plotted expected risk vs return

  - Predicted closing price using LSTM for Nvidia (NVDA)



### Time Series EDA on World War II



- Dataset

  - [Aerial Bombing Operations in World War II](https://www.kaggle.com/datasets/usaf/world-war-ii)

  - [Weather Conditions in World War Two](https://www.kaggle.com/datasets/smid80/weatherww2)



- Notebook

  - [Time Series EDA World War II](https://www.kaggle.com/code/sumeetgedam/time-series-eda-world-war-ii)



- Implementation Points

  - cleaned data to remove uncertainty to ease visualization

  - used scattergeo to see the Bombing path, weather station location

  - weather data was not staionary as per first Dickey-Fuller test

  - used popular methods to get a constant mean

    - moving average

    - differencing method

  - things considered to believe data is now 99% stationary

    - by looking at plot , mean looks constant

    - variance also looked constant

    - test stats from Dickey-Fuller test is smaller that 1% of critical values

  - Forecasted Time Series 

    - used output from differencing method

    - used prediction method ARIMA after finding the constant by ACF, PACF

    - visualized the ARIMA model prediction and got the mean squared error



### Time Series Basics



- Dataset

   - [Air Passengers](https://www.kaggle.com/datasets/rakannimer/air-passengers/data)

   - [Shampoo Sales Dataset](https://www.kaggle.com/datasets/redwankarimsony/shampoo-saled-dataset)

   - [Time Series Data](https://www.kaggle.com/datasets/saurav9786/time-series-data)



- Notebook

   - [Time Series Basics](https://www.kaggle.com/code/sumeetgedam/time-series-basics)



- Implementation Points

    - Explored Univariate and multivariate timeSeries

    - Visualized datasets to understand the Components of TimeSeries

      - Trend

        - Deterministic Trends

        - Stochastic Trends

      - Seasonality

      - Cyclic Patterns

      - Noise

    - Models for Decomposition of TimeSeries

      - Additive Model

        - Additive Decomposition

      - Multiplicative Model

        - Multiplicative Decomposition

    - Visualized the Seasonality in datasets

    - TimeSeries Forecasting Techniques

      - Moving Average

        - Centred Moving Average

        - Trailing Moving Average

    - Handling Missing Values

    - Forcasting Requirements

      - Outliers

      - Resampling

      - Up-sampling

      - Down-sampling

    - Measuring Accuracy

      - Mean Absolute Error

      - Mean Absolute Percentage Error

      - Mean Squared Error

      - Root Mean Square Error

    - ETS ( Error , Trend, Seasonality ) Models

      - SES

        - Simple smoothing with additive errors

      - Holt

        - Holt's linear method with additive errors

          - Double Exponential Smoothing

      - Holt-Winters

        - Holt Winter's linear method with additive errors

          - Multi-step forecast

    - Auto Regressive Models

      - Auto-Correlation function ( ACF ), Partial Auto-Correlation function ( PACF )

      - Stationarity check using Dickey Fuller Test

      - ARIMA Model ( AutoRegressive Integrated Moving Average )

      - Auto ARIMA

        - using AIC ( Akaike Information Criterion ) and BIC (Bayesian Information Criterion ) for model selection



### Spotify Analysis



  - Dataset

    - [Spotify Datasets](https://www.kaggle.com/datasets/lehaknarnauli/spotify-datasets)



  - Notebook

    - [Spotify Analysis](https://www.kaggle.com/code/sumeetgedam/spotify-dataanalysis-and-predictions)



  - Implementation Points

    - The dataset provides audio information for almost 600k Spotify tracks with 20 features

    - Different Visualization including WordCloud, barplot gave us the most popular artist, number of songs per year, most popular songs, etc

    - Plotting histogram and boxplot showed the skewness of features in dataset

    - Added a new feature of song being highly popular if popularity is greater than 50, which was resampled using RandomOverSampler

    - Built a pipeline for columns

      - duration : SimpleImputer, FunctionTransformer, RobustScaler

      - categorical: SimpleImputer, OneHotEncoder

      - numerical columns : SimpleImputer, RobustScaler

    - Used this Pipeline on LogisticRegression, RandomForestClassifier, XGBClassifier to visualize the confusion matric for each of them as heatmap

    - The Importance Feature in each of them were found to be explicit, loudness, explicit column of the dataset. 



### Airbnb Analysis



  - Dataset

    - [NYC Airbnb Dataset](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data)



  - NoteBook

    - [Airbnb Data Anaysis and Prediction](https://www.kaggle.com/code/sumeetgedam/airbnb-dataanalysis-and-prediction)



  - Implemention Points

    - The Dataset describes the listing activity and metrics in NYC for 2019

    - used folium to visualize geographic location on an interactive map

    - Filled NaN values using KNNImputer

    - Analyzed categorical and numerical variable by plot countplots on them

    - Visualized Distribution by Neighbourhood groups which showed Manhattan being the priciest neighbourhood_group for Entire home / apt

    - Analysized outliers and replaced them with threshold which was calculated using quantile 1 and quantile 3

    - Added features and predicted the data using different model 

    - The R2Score, Mean absolute error, Mean squared error, root mean squared error values showed CatBoostRegressor being the best even after Hyperparameter optimization

    - Visualizing the feature importance showed minimum_nights, annual_incoem, total_cost were the top three drivers for this model output



### E-Commerce Analysis



  - Dataset

    - [E-Commerce Data](https://www.kaggle.com/datasets/carrie1/ecommerce-data)



  - Notebook

    - [E-Commerce Data Analysis](https://www.kaggle.com/code/sumeetgedam/ecommerce-data-analysis)



  - Implementation Points

    - Exploring and Columnwise visualization for each column

    - Analyzing negative values to understand the dataset and adding features to it

    - Detecting outliers using scatterplot and quantile

    - Visualizing UnitPrice, Quantity, Sales ( feature )

    - Cleaned data for modeling and Bucketized UnitPrice, Quantity, dates

    - Scaled feature and tested the data on different models:

      - Linear Regression

      - DecisionTree Regessor

      - Random Forest Regression

    - Calculated Mean Absolute Error , Mean Squared Error and  R2 Score of each of them.



### NBA Data Analysis

  - Dataset

    - [NBA Players stats(2023 season)](https://www.kaggle.com/datasets/amirhosseinmirzaie/nba-players-stats2023-season)

  

  - Notebook

    - [NBA Data Analysis](https://www.kaggle.com/code/sumeetgedam/nba-data-analysis)

  

  - Implementation Points

    - Eplored the datataset and cleaned it for ease of use.

    - Used plotly.express and plotly.graph_objects to visualize Players Position with respect to various attributes

    - Dropped columns based on high correlation to improve performance of analysis

    - Modeling the data using: 

      - Linear Regression

      - K-Nearest Neighbors (KNN)

      - Decision Tree Regressor

      - RandomForest Regressor

    - Calculated the r2 score , which showed Linear Regression giving the best value out of all

    - Visual Comparison of Predicted vs Actual Points



###  Premier League Data Analysis



- Dataset

   - [Premier League Player Statistics](https://www.kaggle.com/datasets/rishikeshkanabar/premier-league-player-statistics-updated-daily/data)



- Notebook

   - [Premier League Data Analysis](https://www.kaggle.com/code/sumeetgedam/premier-league-player-s-analysis)



- Implementation Points

    - Explored the dataset and visualized missing values

    - Used plotly.express to visualize : 

      - Countried most represented

      - Players appearance

      - Player's age

    - Used ***plotly.subplots*** to visualize Players Stats by playing position

    - Used ***plotly.graph_objects*** to plot graphs for the way goal was made and the mistakes by players



### Diamond Prices Data Anaysis



- Dataset

   - [Diamonds](https://www.kaggle.com/datasets/shivam2503/diamonds)



- Notebook

   - [Diamond Prices Data Anaysis](https://www.kaggle.com/code/sumeetgedam/data-analysis-on-daimond-prices)



- Implementation Points

   - We started with exploring the dataset which gave us an idea about the attributes present in it.

   - We later made some changes and visualized the following:

     - Carat

     - Cut

     - Color

     - Clarity

     - Depth

     - Dimensions 

     - along with their comparision with Price.

   - Introduced a new feature Volume to see the relationship between volume of diamond and price.

   - Divided the dataset into test and train to use them for evaluating with different Algorithms including :

     - Linear Regression

     - Lasso Regression

     - AdaBoost Regression

     - Ridge Regression

     - GradientBoosting Regression

     - RandomForest Regerssion

     - KNeighours Regression

   - which gave us the ***r2 values*** ( Co-efficient of determination ) and visualized the same which showed us RandomForest Regressor giving the highest r2 value.



### Titanic Data Anaysis



- Dataset

   - [Titanic](https://www.kaggle.com/competitions/titanic)



- Notebook

   - [Titanic Data Anaysis](https://www.kaggle.com/code/sumeetgedam/smg-titanic)



- Implementation Points

   - Reviwed the train and test data provided

   - Calculated the survival rate of men and women

   - Predicted suvival rate using ***RandomForestClassifier***



# Reads



- [Correlation](https://www.investopedia.com/terms/c/correlation.asp)

- [Prophet by Facebook for Forecasting](https://facebook.github.io/prophet/docs/quick_start.html)