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$30 USD / hour
Flag of KENYA
kakamega, kenya
$30 USD / hour
It's currently 2:51 PM here
Joined October 28, 2019
0 Recommendations

Hillary N.

@hillaryjustin73

verified.svg
 4.8 (23 reviews)
4.8
4.8
95%
95%
$30 USD / hour
Flag of KENYA
kakamega, kenya
$30 USD / hour
100%
Jobs Completed
94%
On Budget
94%
On Time
8%
Repeat Hire Rate

Data Analyst | Statistician | BI Developer

A Consultant with over four years of experience in business intelligence, statistical programming, and report writing. An innovative and skilled analyst on the following: ✔️ Data visualization with Tableau, Power BI, and Qlik sense. ✔️ Hypothesis testing, and Machine Learning. ✔️ R markdown reporting, and R shiny app development. ✔️ Database Design (ERD, Normalization, database schema, and Oracle/MySQL/MongoDB/MSSQL/Postgre programming) ✔️ Tutoring in Statistics, and Econometrics. Be assured of quality well commented R code that works, neat and insightful BI dashboards, and well-structured analytics reports to answer your research objectives.
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Portfolio

10880920
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
10880905
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
10880670
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
10880558
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
10880487
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
10880920
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
Train a Naive Bayes model on the frequent features. A Naïve Bayes model uses the Naïve Bayes theorem which is based on an event occurring given that another event has already occurred. The naive Bayes classifier uses the Bayes Theorem. It predicts membership probabilities for each category such as the probability that a given record or data point belongs to a particular group. The class that is selected as the most likely class is the one with maximum posterior probability. In this model, we applied the Bernoulli Naïve Bayes algorithm which analyzes features in binary form. The model was evaluated using a confusion matrix; which shows how the model performed in predicting the polarity of the test reviews. The metrics for that include accuracy, Kappa, sensitivity, and specificity. Accuracy: The was able to predict correctly 79% of the new reviews data. Kappa: The model had 57.99% agreement with the baseline random classifier, this shows that our model agrees with the ground truth
Sentiment Analysis Using Amazon Reviews Data
10880905
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
The study was done to estimate the prevalence of dysmenorrhea among university students and investigate Healthy promoting behaviors associated with dysmenorrhea. The results of this study will be presented under the following headings:- Part (1): Reliability analysis of the data collection tool for measuring dysmenorrhea using the WaLIDD score and also Adolescence Healthy promoting behaviors using the ADH score. Part (2): Socio-demographic characteristics analysis. Part (3): Menstrual information and characteristics. Part (4): The WaLIDD score and pain intensities analysis. Part (5): Analysis of the Adolescent Healthy promoting behaviors. Part (6): The relationship between dysmenorrhea and socio-demographic characteristics. Part (7): The association between dysmenorrhea and menstrual characteristics. Part (8): The relationship between dysmenorrhea and health-promoting behaviors of adolescent girls.
SPSS analytics
10880670
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
COVID 19 cases Forecasting in the USA
Time Series Forecasting Models in R
10880558
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
1.1. Data Gathering The dataset was downloaded from Kaggle, which was mentioned in the web page in the following link: https://www.aihr.com/blog/hr-data-sets-people-analytics/ 1.2. Data Understanding and Cleansing The data set contains the following variables; age, gender, job satisfaction, environment satisfaction, education field, job role, income, overtime, percentage salary hike, tenure, training time, years in a current role, relationship status, and more. The dataset was checked for missing values and it shows it was a complete dataset with no missing values.
Machine learning models in R to predict Employee turnover
10880487
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R
An analysis to investigate whether people with long last names also tend to have long first names The whole analysis is targeted at investigating the relationship between the length of the Arabic first name and the length of the Arabic last name. The analysis begins with dot plots of two variables namely; “Fname” which stands for first name and “Lname” which stands for last name. The dot plot shows that the two variables had no relationship at all. The plotted points indicate that the length of the first name was as long as that of the first name but there was no clear trend of the names. The correlation test confirms this assertion with a very small estimated correlation coefficient. The regression model also confirms this with a huge p-value. Therefore it’s statistically correct to state that people with long last names don’t necessarily have long first names.
Hypotheses Testing in R

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$90.00 USD
time series analysis
he did a great job in my project
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Project 32100573 has been deleted
I'd definitely hire Hillary again
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Hillary has an amazing understanding of Machine Learning algorithms, he could turn around my project in desired time & budget. Would definitely be working with him again.
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solve a problem using R language
Absolutely Perfect work. Thank you!
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Someone well experienced with Sentiment Analysis
Excellent freelancer. I will work with him again.
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@rk0737488
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3 years ago

Experience

Data Scientist

Smart Applications International Group Ltd.
Jan 2022 - Present
1. Extracted and interpreted data patterns to translate findings into actionable outcomes. 2. Provided technical support for troubleshooting analytics and reporting issues. 3. Used statistical methods to analyze data and generate useful business reports. 4. Developed and implemented data governance policies and procedures. 5. Deployed predictive analytics models to forecast future trends. 6. Designed and developed data pipelines to acquire, clean and process data.

Data Scientist

Freelancer
Nov 2019 - Present
1. Developed intricate algorithms based on deep-dive statistical analysis and predictive data modeling. 2. Coached and mentored junior data scientists on Python and data mining techniques. 3. Modeled predictions with feature selection algorithms. 4. Analyzed large datasets to identify trends and patterns in customer behaviors. 5. Devised and deployed predictive models using machine learning algorithms to drive business decisions.

Lead Data Analyst

Virtual Support Services
Jan 2021 - Dec 2021 (11 months, 1 day)
1. Created and automated data visualizations to present insights and tell compelling stories. 2. Used business objects, business intelligence and other reporting tools to extract data from data solutions and data warehouses. 3. Interpreted business questions and collaborated with engineering teams to identify data sources. 4. Implemented business intelligence solutions to increase operational efficiency. 5. Created dashboards to monitor and track key performance indicators.

Education

STATISTICS

Jomo Kenyatta University of Agriculture and Technology, Kenya 2014 - 2018
(4 years)

Qualifications

Oracle Cloud Platform Data Integration Associate 2018

Oracle
2019
Completed a course in Data integration services where I acquired skills in Data migration, Relational database management, ETL development, Data quality & governance and Data modelling.

IBM Advance Data Science Professional

IBM
2019
Deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. Great understanding of mathematical foundations behind all machine learning & deep learning algorithms. I can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

Bachelors Degree in Statistics

Jomo Kenyatta University of Agriculture and Technology
2018
A professional Statistician with credentials in Quantitative/Qualitative methods, Research methodology, Statistical programming, Regression modelling, Sampling methods, Econometrics, Time series analysis, Biostatistics, and Survival Analysis.

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Certifications

SQL_1.png SQL 1 80%

Top Skills

R Programming Language 20
Statistics 16
Statistical Analysis 14
SPSS Statistics 9
Data Processing 6

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