Publications by Umer Farooq
Exponential Smoothing Forecast
1. Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. pigs <- aus_livestock|> filter(State == 'Victoria', Animal == 'Pigs') autoplot(pigs, Count)+ labs(y = "Count" , x = "Date" , title = "Pigs Slaughtered, Victoria") a. Use the ETS() function to estimate the equivalent model for...
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Data Preprocessing
2.1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. The data can be accessed via: library(mlbe...
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The Forecaster's Toolbox
1. Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy) Bricks (aus_production) NSW Lambs (aus_livestock) Household wealth (hh_budget). Australian takeaway food turnover (aus_retail). Answer: Australian Population (global_econo...
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Time Series Decomposition
Time Series Decomposition Umer Farooq 2024-02-11 1. Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? global_economy |> autoplot(GDP/Population) + labs(title= "GDP per capita", y = "$US")+theme(legend.position = 'none...
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Time Series Plots
Exercises 1. Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. A) Use ? (or help()) to find out about the data in each series. Answer: Let’s load the library first library(fpp3) ## ── Attaching packages ────────────────────�...
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Predicting Wine Sales With Certain Properties
Predicting Wine Sales With Certain Properties Predicting Wine Sales With Certain Properties INTRODUCTION: DATA EXPLORATION: DATA PREPARATION: BUILDING MODELS: SELECTING MODELS AND EVALUATION: CONCLUSION: Umer Farooq 2023-12-14 INTRODUCTION: In this study we will explore, analyze and model a data set containing informat...
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Predicting The Probability Of A Car Crash And It's Cost
Predicting The Probability Of A Car Crash And It’s Cost Predicting The Probability Of A Car Crash And It’s Cost INTRODUCTION: 1. DATA EXPLORATION: 2. DATA PREPARATION 3. BUILDING AND SELECTING MODELS : 4. EVALUATION 5. CONCLUSION: Umer Farooq 2023-12-01 INTRODUCTION: In this study, we will explore, an...
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Predicting Whether The Neighborhood Will Be At Risk For High Crime Levels Using Logistics Regression
Predicting Whether The Neighborhood Will Be At Risk For High Crime Levels Using Logistics Regression Predicting Whether The Neighborhood Will Be At Risk For High Crime Levels Using Logistics Regression INTRODUCTION: DATA EXPLORATION: DATA PREPARATION: BUILD MODELS MODEL SELECTION: CONCLUSION: APPENDIX: Umer Farooq 2023-...
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Classification Model Metrics
Introduction: Loading Data: Identifying the Columns: Accuracy: Classification Error Rate: Precision: Sensitivity: Specificity: F1 Score: F1 Score Bounds: ROC Curve and AUC: Classification metrics: Investigating caret Package: Investigating pR...
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Predicting Wins for Baseball Game Using Multiple Linear Regression
Predicting Wins for Baseball Game Using Multiple Linear Regression Predicting Wins for Baseball Game Using Multiple Linear Regression INTRODUCTION: DATA EXPLORATION: DATA PREPARATION: BUILDING MODELS: SELECTING MODELS AND PREDICTING: CONCLUSION: APPENDIX: Umer Farooq 2023-09-26 INTRODUCTION: In this particular p...
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