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KENYA的国旗
kitengela, kenya
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目前这里是11:48 上午
十二月 13, 2017已加入
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Glen K.

@Libraglen8

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Machine Learning Scientist

I am machine learning scientist with a passion for solving complex data problems using Python. I specialize in designing and implementing machine learning models that help businesses make better data-driven decisions. Whether you need help with data preprocessing, feature engineering, model selection, or model optimization, I've got you covered. I have extensive experience working with a variety of data types, including structured, semi-structured, and unstructured data. I am proficient in using Python libraries such as TensorFlow, Keras, scikit-learn, pandas, and NumPy to build and deploy machine learning models. I'm also skilled in data visualization and exploratory data analysis using tools like Matplotlib, Seaborn, and Plotly. My goal is to help you turn your data into actionable insights that drive your business forward. I'm committed to delivering high-quality work that meets your specific needs and exceeds your expectations. You can check me out at: [login to view URL] Let's work together to unlock the full potential of your data. Get in touch today to discuss your project!
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11861259
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
11861254
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
11861247
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
11861218
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
11861203
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
11861210
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
11861259
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
This project delved into the demographic trends of Nobel Prize winners from 1901 to 2016. Initial data inspection revealed a shift in dominance from Europe to the USA over time. Men consistently outnumbered women in laureates, with categories like physics and economics showing significant gender imbalances. Marie Curie, winning the Physics Nobel in 1903, was the first woman laureate. Age analysis showed laureates getting older, with exceptions in the peace category where winners were younger. Visualization highlighted evolving trends across prize categories, including age disparities and changes in laureate demographics.
NOBEL PRIZE ANALYSIS
11861254
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
This project aimed to assess the claim of declining movie durations on Netflix. Initial data from 2011-2020 hinted at a decrease. Expanding data collection, a dataset with 8000 entries was curated, featuring title, country, genre, release year, and duration. Visualizations revealed shorter films, notably in genres like children's and documentaries, contributing to the perceived decline. This analysis shed light on movie duration trends, providing insights into Netflix's content landscape.
NETFLIX ANALYSIS
11861247
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
This project aimed to develop a system capable of detecting various diseases such as Cancer, Heart Disease, Diabetes, or Parkinson's, providing quick and accurate assessments to aid medical professionals in making informed decisions. The user-friendly app allows users to input relevant features, and the system returns predictions based on trained models for each disease. Data preparation involved loading trained models for each disease and presenting users with a homepage to select the disease they wish to assess. Upon selection, users fill out a form with disease-specific details, which are encoded, scaled, and transformed before being passed to the corresponding model for prediction. Results are displayed to users, indicating whether they are predicted to have the disease. In conclusion, the Disease Detection project successfully applies machine learning techniques to assist healthcare. The system's ability to generalize to new instances allows for timely and accurate assessments.
MULTIPLE DISEASE DETECTION SYSTEM
11861218
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
This project aimed to develop a machine learning model for predicting health insurance charges based on factors like age, gender, BMI, number of children, smoking habit, and region, using data collected from the US region. After ensuring data completeness and handling outliers, numerical feature scaling was applied for uniformity. Exploratory data analysis was conducted to understand data distribution and identify correlations. Feature engineering was performed, including smoker status and BMI category extraction. The dataset was split into training and testing sets, with model evaluation using mean absolute error (MAE) and R-squared metrics. The model achieved a satisfactory MAE of 2,452 and an R-squared score of 0.78, indicating good fit. Notably, smoking status and BMI category emerged as significant predictors of medical charges. Future work could involve exploring additional factors like occupation, education, or lifestyle to enhance model performance further.
MEDICAL COST INSURANCE PREDICTION
11861203
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
This project aims to detect fraudulent credit card transactions using the Credit Card Fraud Detection dataset from Kaggle, comprising transaction data from European credit cardholders. Initial exploration reveals no correlation between transaction amount and fraud status, with higher amounts typically associated with legitimate transactions. After preprocessing, including dropping the 'Time' column and scaling 'Amount' using StandardScaler, Logistic Regression is selected for its simplicity and efficiency in binary classification. The model achieves 94.4% accuracy on the training set and 93.8% on the testing set, with 85% recall and 89% precision for fraudulent transactions. While effective, the model's performance may be limited on datasets with more complex feature relationships. Future work may involve exploring more sophisticated models like Random Forest or Gradient Boosting and collecting additional data on fraudulent transactions to enhance detection capabilities.
CREDIT CARD FRAUD DETECTION
11861210
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS
This project aimed to cluster customers based on their annual income and spending score, facilitating targeted marketing strategies for improved customer satisfaction and revenue. Using the "Mall Customer Segmentation Data," I performed exploratory data analysis, employing the elbow method to determine the optimal number of clusters (5) for the K-Means algorithm. After training the model, I visually inspected the resulting clusters on a scatter plot to glean insights. The clusters identified distinct customer groups based on income and spending behavior. High-income, high-spending customers formed one segment, while low-income, low-spending customers comprised another. The analysis enables tailored marketing approaches for each segment, potentially enhancing business outcomes. Future work may involve incorporating additional features or demographic data for deeper insights into customer behavior.
CUSTOMER SEGMENTATION ANALYSIS

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经验

Machine Learning Scientist

Kaggle
1月 2017 - 现在
Analyzed problems, datasets, and evaluation criteria to understand the task. Explored and preprocess datasets, ensuring data quality. Performed feature engineering, model selection, and hyperparameter optimization to improve model performance. Documented and presented my methodologies well Stayed up to date with the latest advancements and trends in machine learning Shared my knowledge and experience with the wider data science community

Machine Learning Scientist

Freelance(self-employed)
1月 2017 - 现在
I create customized applications for predictions, automation, and decision-making, as well as optimization algorithms. I leverage machine learning algorithms to enhance existing products. I also identify new problem areas and conduct detailed research to create innovative solutions. I transform raw data to align with machine learning assumptions and efficiently compile, clean data. I devise and deploy predictive models using machine learning algorithms to drive data informed decisions.

ICT Intern Data Analyst

Youth Enterprise Fund
4月 2021 - 12月 2021 (8 个月, 2 天)
Completed paperwork, recognizing discrepancies and promptly addressing for resolution. Demonstrated leadership skills in managing projects from concept to completion. Cultivated interpersonal skills by building positive relationships with others. I was given the opportunity to analyze and derive insights from the organization's data. I worked with large datasets, applying statistical methods and data visualization techniques to identify patterns, trends, and correlations.

教育

Bsc. in Computer Science

Daystar University, Kenya 2021 - 2023
(2 年)

Diploma In ICT

Daystar University, Kenya 2019 - 2021
(2 年)

Mobile Software Development and Entrepreneurship Program

Kenya 2018
（少于一年）

资质

Applied Data Science II: Machine Learning & Statistical Analysis (with honors)

WorldQuant University
2022
Earners of this badge are able to build machine learning models to make predictions on real-world data. They understand the best way to treat, clean, and encode data and how to choose the appropriate machine learning models for the task. They can properly tune the model to create a generalized model that performs well on both a training set and on out-of-sample data. They can build models using text and time series data. Earners are also proficient in using Python’s scikit-learn package.

Applied Data Science I: Scientific Computing & Python

WorldQuant University
2021
Earners of this badge have completed a comprehensive introduction to scientific computing, Python, and related tools used by data scientists. They can use Python to read, clean, process, and analyze real-world data by following good programming practices such as using functions, choosing the appropriate data structures, and writing readable, maintainable code. To determine the statistical significance of the results of their analysis, they can apply statistical analysis and hypothesis testing.

Fundamental Understanding on Machine Learning

Kaggle
2021
This course provided a solid foundation in machine learning concepts. I learned about core ideas, model building, and practical implementation. Topics covered include data exploration, model validation, and understanding how machine learning models work. The skills gained are essential for anyone entering the field of data science.

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