From e352fc4b92e56d711758471833d6d043b79424d5 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Fri, 18 Oct 2024 21:06:08 +0530 Subject: [PATCH 01/13] Create README.md --- .../Customer Segmentation Analysis/README.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 Data Analysis/Customer Segmentation Analysis/README.md diff --git a/Data Analysis/Customer Segmentation Analysis/README.md b/Data Analysis/Customer Segmentation Analysis/README.md new file mode 100644 index 000000000..01504f1d2 --- /dev/null +++ b/Data Analysis/Customer Segmentation Analysis/README.md @@ -0,0 +1,19 @@ +# Customer Segmentation AnalysisπŸ” +Customer segmentation is the process of dividing customers into distinct groups based on shared characteristics. This project aims to analyze customer data to identify meaningful segments that help businesses tailor their marketing strategies, improve customer service, and optimize product offerings. + +### Objective : + +- Understand customer behavior and preferences. +- Identify key segments based on demographics, purchasing habits, or other relevant features. +- Provide insights that can assist in targeted marketing, personalized recommendations, and better business decision-making. +### Approach : + +- Data Collection & Preprocessing: Collect and clean the data to ensure it is suitable for analysis. +- Feature Selection: Identify key variables that can help differentiate customer groups, such as age, location, spending habits, and product preferences. +- Clustering Algorithm: Use machine learning techniques (like K-Means, Hierarchical Clustering, etc.) to segment customers. +- Evaluation & Visualization: Assess the effectiveness of the segmentation and visualize the results for better understanding. + + + To view the Analysis πŸ‘‰ [Customer_Segmentation_Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Customer%20Segmentation.ipynb) + + To view the Dataset πŸ‘‰ [Customer_Segmentation.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Shopping%20Mall%20Customer%20Segmentation%20Data%20.csv) From fffb55f61ca22032553076daeee7d575c65a4c7b Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Fri, 18 Oct 2024 21:41:38 +0530 Subject: [PATCH 02/13] Create README.md --- .../README.md | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 Data Analysis/Daily News and Stock Data Analysis/README.md diff --git a/Data Analysis/Daily News and Stock Data Analysis/README.md b/Data Analysis/Daily News and Stock Data Analysis/README.md new file mode 100644 index 000000000..c18edd8af --- /dev/null +++ b/Data Analysis/Daily News and Stock Data Analysis/README.md @@ -0,0 +1,23 @@ +# Daily news and stock Data Analysis πŸ“ˆ + +The financial markets are influenced by a multitude of factors, including daily news events. This project seeks to analyze the dynamic relationship between news headlines and stock price fluctuations. + +### Project Goals: + +- Real-Time Analysis: Track and analyze daily news articles alongside stock market data to observe how real-time events impact stock prices. +- Trend Identification: Identify recurring trends by analyzing historical data to see which types of news have the most significant effects on market movements. +- Risk Assessment: Assess how various categories of news, from earnings reports to geopolitical events, contribute to stock volatility. + +### Methodology: + +- Data Aggregation: Collect historical stock data and news articles from various sources. Integrate real-time data feeds to ensure up-to-date information. +- Feature Engineering: Extract key attributes from news articles, such as sentiment, keywords, and event type, to use as predictors. +- Exploratory Data Analysis (EDA): Use visualizations to examine correlations between news sentiment, trading volume, and stock prices. +- Machine Learning Models: Develop models to analyze the impact of news sentiment on stock price trends. Utilize techniques such as regression analysis, time series forecasting, and classification to predict price movements. +- Backtesting: Test the models on historical data to validate their accuracy and refine strategies for real-world application. + + Outcome⚑ - It can provide businesses and investors with a deeper understanding of how global events which are affecting the market behavior. + + ## To view the analysis πŸ‘‰ [DailyNews.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Daily%20News%20and%20Stock%20Data%20Analysis/DailyNews.ipynb) + + ## To view the combined dataset πŸ‘‰ [Combined Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Daily%20News%20and%20Stock%20Data%20Analysis/Dataset/Combined_News_DJIA.csv) From 216b89cd4789a3a170af42a79eb80e5b8feed4ed Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Fri, 18 Oct 2024 21:42:34 +0530 Subject: [PATCH 03/13] Update README.md --- Data Analysis/Customer Segmentation Analysis/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Data Analysis/Customer Segmentation Analysis/README.md b/Data Analysis/Customer Segmentation Analysis/README.md index 01504f1d2..74f491eef 100644 --- a/Data Analysis/Customer Segmentation Analysis/README.md +++ b/Data Analysis/Customer Segmentation Analysis/README.md @@ -14,6 +14,6 @@ Customer segmentation is the process of dividing customers into distinct groups - Evaluation & Visualization: Assess the effectiveness of the segmentation and visualize the results for better understanding. - To view the Analysis πŸ‘‰ [Customer_Segmentation_Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Customer%20Segmentation.ipynb) + ## To view the Analysis πŸ‘‰ [Customer_Segmentation_Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Customer%20Segmentation.ipynb) - To view the Dataset πŸ‘‰ [Customer_Segmentation.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Shopping%20Mall%20Customer%20Segmentation%20Data%20.csv) + ## To view the Dataset πŸ‘‰ [Customer_Segmentation.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Customer%20Segmentation%20Analysis/Shopping%20Mall%20Customer%20Segmentation%20Data%20.csv) From 68b7aa9de2324238191b5a2b206abf0084e11894 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Fri, 18 Oct 2024 23:21:23 +0530 Subject: [PATCH 04/13] Create README.md --- .../FIFA 19 Dataset Analysis/README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 Data Analysis/FIFA 19 Dataset Analysis/README.md diff --git a/Data Analysis/FIFA 19 Dataset Analysis/README.md b/Data Analysis/FIFA 19 Dataset Analysis/README.md new file mode 100644 index 000000000..2f30443bb --- /dev/null +++ b/Data Analysis/FIFA 19 Dataset Analysis/README.md @@ -0,0 +1,18 @@ +# FIFA 19 Data Analysis⛹️‍♂️ + +This project dives into the world of FIFA 19 which is a popular football game, to analyze player attributes, team dynamics, and overall game performance. By exploring the rich dataset, we aim to uncover patterns, trends, and insights that can help players, gamers, and analysts understand what makes a player or team successful in the game. + +### Objective +- Analyze player attributes to determine what characteristics contribute to high overall ratings. +- Explore team compositions and find patterns that lead to better performance on the field. +- Understand the relationship between various in-game attributes like speed, strength, and skills, and how they affect gameplay. + +### Outcome : +- Analysts & Researchers: Study the underlying mechanics of the game and how it mirrors real-world football strategies. +- Gaming Community: Provide insights to help players build better teams in various game modes like Ultimate Team or Career Mode. + +## To view the analysis πŸ‘‰ [FIFA 19 Analysis](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/FIFA%2019%20Dataset%20Analysis/Model/FIFA_19_Dataset_Analysis.ipynb) + +## To view the Dataset πŸ‘‰ [FIFA_19.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/FIFA%2019%20Dataset%20Analysis/DataSet/Data.csv) + +For more information please refer to [Model](https://github.com/Archi20876/machine-learning-repos/tree/main/Data%20Analysis/FIFA%2019%20Dataset%20Analysis/Model) From 7ab69f28f7c8db50ad481016a518a2565e1f6838 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Fri, 18 Oct 2024 23:38:14 +0530 Subject: [PATCH 05/13] Create README.md --- .../Github-Automated-Analysis/README.md | 32 +++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 Data Analysis/Github-Automated-Analysis/README.md diff --git a/Data Analysis/Github-Automated-Analysis/README.md b/Data Analysis/Github-Automated-Analysis/README.md new file mode 100644 index 000000000..b3777f4be --- /dev/null +++ b/Data Analysis/Github-Automated-Analysis/README.md @@ -0,0 +1,32 @@ +# Lettuce Growth Analysis πŸ₯¬ + +This project aims to analyze the factors influencing lettuce growth, focusing on how different conditions impact yield, quality, and growth rate. By understanding the variables affecting lettuce cultivation, we can optimize farming practices, improve productivity, and reduce resource usage, benefiting both small-scale farmers and larger agricultural enterprises. + +### Objectives : + +- Assess Growth Stages: Monitor and analyze different growth stages of lettuce (seedling, vegetative, maturity) to determine the conditions required at each phase for optimal development. +- Impact of Fertilizers: Study the effect of various fertilizers (organic and synthetic) on lettuce growth, yield, and nutrient content to determine the best fertilization strategies. +- Pest and Disease Resistance: Analyze how environmental conditions and soil quality affect lettuce susceptibility to pests and diseases, helping to develop preventive measures. +- Nutritional Analysis: Evaluate the nutritional content (e.g., vitamins, minerals) of lettuce under different growth conditions to ensure the produce meets quality standards. +- Climate Adaptability: Investigate how changes in climate, such as temperature fluctuations and increased humidity, impact lettuce growth and how to mitigate adverse effects. +- Growth Rate Optimization: Identify key factors that accelerate or hinder lettuce growth, allowing for faster production cycles without sacrificing quality. + +### Approach : + +- Data Collection: Gather data from controlled experiments or field studies, including variables like temperature, humidity, soil type, pH, water usage, and growth measurements (e.g., height, leaf count, weight). +- Data Cleaning & Preparation: Process and clean the data to ensure accuracy, handle missing values, and standardize units across datasets. +- Exploratory Data Analysis (EDA): +Use visualizations to identify trends between environmental factors and lettuce growth (e.g., scatter plots showing the relationship between soil pH and leaf count). +Analyze the influence of different soil types and nutrient levels on lettuce yield. + +- Predictive Modeling: Develop machine learning models to predict lettuce growth under different conditions, helping to identify the most favorable settings for cultivation. +- Optimization: Use the insights from the analysis to suggest optimal growing conditions, including ideal soil preparation, watering schedules, and environmental controls + +### Applications: + +Farmers & Growers: Optimize farming practices to improve lettuce yield and reduce costs. +Agricultural Researchers: Gain insights into the factors affecting plant growth, enabling further research into sustainable farming. +Agri-Tech Companies: Develop automated solutions to monitor and adjust environmental conditions for lettuce cultivation. + +## To view the Analysis πŸ‘‰ [Lettuce Growth Analysis](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/lettuce_growth.ipynb) +## To view the Dataset πŸ‘‰ [Lettuce Growth.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/Dataset/lettuce_dataset_updated.csv) From a75ba8c60e3bc90929f8e3a35917c269a1f4ac60 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sat, 19 Oct 2024 09:58:22 +0530 Subject: [PATCH 06/13] Delete Data Analysis/Github-Automated-Analysis/README.md --- .../Github-Automated-Analysis/README.md | 32 ------------------- 1 file changed, 32 deletions(-) delete mode 100644 Data Analysis/Github-Automated-Analysis/README.md diff --git a/Data Analysis/Github-Automated-Analysis/README.md b/Data Analysis/Github-Automated-Analysis/README.md deleted file mode 100644 index b3777f4be..000000000 --- a/Data Analysis/Github-Automated-Analysis/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Lettuce Growth Analysis πŸ₯¬ - -This project aims to analyze the factors influencing lettuce growth, focusing on how different conditions impact yield, quality, and growth rate. By understanding the variables affecting lettuce cultivation, we can optimize farming practices, improve productivity, and reduce resource usage, benefiting both small-scale farmers and larger agricultural enterprises. - -### Objectives : - -- Assess Growth Stages: Monitor and analyze different growth stages of lettuce (seedling, vegetative, maturity) to determine the conditions required at each phase for optimal development. -- Impact of Fertilizers: Study the effect of various fertilizers (organic and synthetic) on lettuce growth, yield, and nutrient content to determine the best fertilization strategies. -- Pest and Disease Resistance: Analyze how environmental conditions and soil quality affect lettuce susceptibility to pests and diseases, helping to develop preventive measures. -- Nutritional Analysis: Evaluate the nutritional content (e.g., vitamins, minerals) of lettuce under different growth conditions to ensure the produce meets quality standards. -- Climate Adaptability: Investigate how changes in climate, such as temperature fluctuations and increased humidity, impact lettuce growth and how to mitigate adverse effects. -- Growth Rate Optimization: Identify key factors that accelerate or hinder lettuce growth, allowing for faster production cycles without sacrificing quality. - -### Approach : - -- Data Collection: Gather data from controlled experiments or field studies, including variables like temperature, humidity, soil type, pH, water usage, and growth measurements (e.g., height, leaf count, weight). -- Data Cleaning & Preparation: Process and clean the data to ensure accuracy, handle missing values, and standardize units across datasets. -- Exploratory Data Analysis (EDA): -Use visualizations to identify trends between environmental factors and lettuce growth (e.g., scatter plots showing the relationship between soil pH and leaf count). -Analyze the influence of different soil types and nutrient levels on lettuce yield. - -- Predictive Modeling: Develop machine learning models to predict lettuce growth under different conditions, helping to identify the most favorable settings for cultivation. -- Optimization: Use the insights from the analysis to suggest optimal growing conditions, including ideal soil preparation, watering schedules, and environmental controls - -### Applications: - -Farmers & Growers: Optimize farming practices to improve lettuce yield and reduce costs. -Agricultural Researchers: Gain insights into the factors affecting plant growth, enabling further research into sustainable farming. -Agri-Tech Companies: Develop automated solutions to monitor and adjust environmental conditions for lettuce cultivation. - -## To view the Analysis πŸ‘‰ [Lettuce Growth Analysis](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/lettuce_growth.ipynb) -## To view the Dataset πŸ‘‰ [Lettuce Growth.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/Dataset/lettuce_dataset_updated.csv) From 83bc9e2c6fffb0a327ce1d098f172472482dbe3a Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sat, 19 Oct 2024 09:59:58 +0530 Subject: [PATCH 07/13] Create README.md --- .../README.md | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 Data Analysis/Lettuce growth analysis and prediction/README.md diff --git a/Data Analysis/Lettuce growth analysis and prediction/README.md b/Data Analysis/Lettuce growth analysis and prediction/README.md new file mode 100644 index 000000000..2a837a96f --- /dev/null +++ b/Data Analysis/Lettuce growth analysis and prediction/README.md @@ -0,0 +1,34 @@ +# Lettuce Growth Analysis πŸ₯¬ + +This project aims to analyze the factors influencing lettuce growth, focusing on how different conditions impact yield, quality, and growth rate. By understanding the variables affecting lettuce cultivation, we can optimize farming practices, improve productivity, and reduce resource usage, benefiting both small-scale farmers and larger agricultural enterprises. + +### Objectives : + +- Assess Growth Stages: Monitor and analyze different growth stages of lettuce (seedling, vegetative, maturity) to determine the conditions required at each phase for optimal development. +- Impact of Fertilizers: Study the effect of various fertilizers (organic and synthetic) on lettuce growth, yield, and nutrient content to determine the best fertilization strategies. +- Pest and Disease Resistance: Analyze how environmental conditions and soil quality affect lettuce susceptibility to pests and diseases, helping to develop preventive measures. +- Nutritional Analysis: Evaluate the nutritional content (e.g., vitamins, minerals) of lettuce under different growth conditions to ensure the produce meets quality standards. +- Climate Adaptability: Investigate how changes in climate, such as temperature fluctuations and increased humidity, impact lettuce growth and how to mitigate adverse effects. +- Growth Rate Optimization: Identify key factors that accelerate or hinder lettuce growth, allowing for faster production cycles without sacrificing quality. + +### Approach : + +- Data Collection: Gather data from controlled experiments or field studies, including variables like temperature, humidity, soil type, pH, water usage, and growth measurements (e.g., height, leaf count, weight). +- Data Cleaning & Preparation: Process and clean the data to ensure accuracy, handle missing values, and standardize units across datasets. +- Exploratory Data Analysis (EDA): +Use visualizations to identify trends between environmental factors and lettuce growth (e.g., scatter plots showing the relationship between soil pH and leaf count). +Analyze the influence of different soil types and nutrient levels on lettuce yield. + +- Predictive Modeling: Develop machine learning models to predict lettuce growth under different conditions, helping to identify the most favorable settings for cultivation. +- Optimization: Use the insights from the analysis to suggest optimal growing conditions, including ideal soil preparation, watering schedules, and environmental controls. + +### Applications: + +- Farmers & Growers: Optimize farming practices to improve lettuce yield and reduce costs. + +- Agricultural Researchers: Gain insights into the factors affecting plant growth, enabling further research into sustainable farming. + +- Agri-Tech Companies: Develop automated solutions to monitor and adjust environmental conditions for lettuce cultivation. + +## To view the Analysis πŸ‘‰ [Lettuce Growth Analysis](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/lettuce_growth.ipynb) +## To view the Dataset πŸ‘‰ [Lettuce Growth.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Lettuce%20growth%20analysis%20and%20prediction/Dataset/lettuce_dataset_updated.csv) From 6a3a3668d45eb38f875f20d66553c6055e8dfa41 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sat, 19 Oct 2024 10:22:11 +0530 Subject: [PATCH 08/13] Create README.md --- .../README.md | 46 +++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 Data Analysis/Plant growth analysis and prediction/README.md diff --git a/Data Analysis/Plant growth analysis and prediction/README.md b/Data Analysis/Plant growth analysis and prediction/README.md new file mode 100644 index 000000000..2a1dbcf70 --- /dev/null +++ b/Data Analysis/Plant growth analysis and prediction/README.md @@ -0,0 +1,46 @@ +# Plant Growth Analysis ☘️ + +This project aims to explore the key factors that affect plant growth, focusing on identifying the best conditions for achieving maximum yield, health, and sustainability across a variety of plant species. By examining environmental, soil, and biological variables, the analysis seeks to uncover valuable insights that can enhance agricultural practices, promote urban gardening, and support sustainable farming methods. + +### Objectives: + +- Study Growth Conditions: Examine how environmental factors like light, temperature, humidity, and air quality affect plant growth rates and health. + +- Soil Quality Analysis: Investigate the impact of soil properties, such as pH, texture, and nutrient levels, on the growth of different plant species. + +- Water Management: Explore the effects of different watering techniques and frequencies on plant health, aiming to find the most water-efficient practices. + +- Impact of Fertilizers & Nutrients: Assess how various fertilizers (organic vs. chemical) and nutrient supplements influence growth, yield, and plant vitality. + +- Growth Rate Optimization: Identify conditions that accelerate growth without compromising quality, helping to shorten cultivation cycles. + +- Climate Adaptation: Study how plants react to varying climates, including temperature extremes, and develop strategies for enhancing resilience to climate change. + +### Approach: + +- Data Collection: Gather data from datasets on various factors, including soil quality, water usage, and plant health metrics. + +- Data Cleaning & Preparation: Process and clean the data to ensure consistency and accuracy, handle missing values, and prepare the dataset for analysis. + +- Exploratory Data Analysis (EDA): + Visualize relationships between environmental variables (e.g., Sunlight, temperature,soil) and plant growth milestone. + Analyze the effect of soil properties and watering schedules on plant health and yield. + +- Predictive Modeling: Develop machine learning models to predict plant growth outcomes based on different input conditions, helping to simulate various scenarios. + +- Optimization Strategies: Use insights from the analysis to suggest best practices for plant cultivation, including ideal soil compositions, watering schedules, and climate settings. + + +### Applications: + +- Farmers & Gardeners: Enhance crop yield and plant health by understanding the ideal conditions for growth. + +- Agricultural Researchers: Develop new insights into plant physiology and ways to improve agricultural practices. + +- Environmental Conservation: Promote sustainable agriculture practices that can reduce resource usage and protect soil health. + + ## To view the Analysis πŸ‘‰ [Plant Growth Analysis](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Plant%20growth%20analysis%20and%20prediction/plant_growth.ipynb) + + ## To view the Dataset πŸ‘‰ [Plant Growth.csv](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Plant%20growth%20analysis%20and%20prediction/Dataset/plant_growth_data.csv) + + From 774e9ef0619af0bdc307a16ad31263e39a27ce0f Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sun, 20 Oct 2024 10:50:27 +0530 Subject: [PATCH 09/13] Create README.md --- .../README.md | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md diff --git a/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md b/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md new file mode 100644 index 000000000..28f13fa66 --- /dev/null +++ b/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md @@ -0,0 +1,35 @@ +# Sentiment Analysis πŸ˜Šβ˜ΉοΈπŸ˜‘ + +This project focuses on sentiment analysis, a technique used to determine the emotional tone behind a body of text. By analyzing data from various sources such as social media, customer reviews, and online forums, the project aims to classify text as positive, negative, or neutral. Understanding sentiment can help businesses gauge customer opinions, improve products, and enhance customer satisfaction. + +### Objectives : + +- Classify Sentiments: Develop models to accurately classify text into categories like positive, negative, and neutral. + +- Understand Customer Feedback: Analyze customer reviews to identify key sentiments associated with products, services, or brands. + +- Monitor Public Opinion: Track sentiment on social media platforms to understand public reactions to events, campaigns, or trends. + +- Improve Decision-Making: Provide actionable insights to businesses, enabling them to make informed decisions based on customer feedback and market sentiment. + +### Approach : +- Data Cleaning & Preprocessing: Clean the data to remove noise (e.g., hashtags, URLs, special characters), and preprocess it by tokenizing, removing stopwords, and lemmatizing the text. +- Exploratory Data Analysis (EDA): +Visualize the distribution of sentiments in the dataset. +Identify common words and phrases associated with positive and negative sentiments. +Analyze sentiment over time to observe trends. + Model Development: Use machine learning algorithms such as Naive Bayes, Multinomial Naive Bayes,Random Forest Classifier , Logistics Regression or deep learning techniques like LSTM to build a sentiment classification model. +- Sentiment Scoring: Assign sentiment scores to text to quantify the degree of positivity or negativity. +- Evaluation & Optimization: Evaluate model performance with precision metrics. + +### Applications : + +Businesses: Use predictive insights to improve products, services, and customer engagement strategies. +Marketing Teams: Forecast the success of campaigns and adjust strategies based on predicted trends. +Social Media Monitoring: Track real-time sentiment and predict future public reactions to events, products, or announcements. + +To view the Analysis πŸ‘‰ [Sentiment Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Sentiment%20Analysis.ipynb) + +To view More charts in the Analysis πŸ‘‰ [Sentiment analysis charts](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/ChartsForBetterUnderstanding.ipynb) + +To view the Dataset πŸ‘‰ [Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Headlines.csv) From 4f827624c9a84a9f9065ddf358dcd5029af86dbe Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sun, 20 Oct 2024 20:47:42 +0530 Subject: [PATCH 10/13] Create README.md --- .../README.md | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 Data Analysis/Time Series Forecasting on Global Warming/README.md diff --git a/Data Analysis/Time Series Forecasting on Global Warming/README.md b/Data Analysis/Time Series Forecasting on Global Warming/README.md new file mode 100644 index 000000000..62397230b --- /dev/null +++ b/Data Analysis/Time Series Forecasting on Global Warming/README.md @@ -0,0 +1,31 @@ +# Time Series Forecasting on Global WarmingπŸ“ˆ + +This project focuses on using time series forecasting techniques to analyze and predict future trends related to global warming. By examining historical data on global temperatures, carbon emissions, and other climate-related variables, the project aims to model future climate patterns and provide insights into the ongoing issue of climate change. Understanding these trends can help policymakers, scientists, and environmentalists take proactive measures to address the challenges of global warming. + +### Approach: + +- Data Collection: Compile historical climate data from reputable sources, such as NASA, NOAA, and the IPCC, including global temperature records, CO2 concentrations, and oceanic measurements. + +- Data Cleaning & Preparation: Process and clean the data to remove anomalies, handle missing values, and standardize variables for accurate analysis. + +- Exploratory Data Analysis (EDA):Analyze seasonal variations and anomalies in the data to understand how certain periods or events affect climate trends. + +- Time Series Forecasting Models:Develop forecasting models with Prophet to predict future global temperatures and other climate-related metrics. +Compare model performance to select the best approach for accurate long-term forecasts. + +- Evaluation & Optimization: Assess model accuracy using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and optimize models for better predictions. + + +### Applications: + +- Environmental Organizations: Raise awareness about global warming trends and advocate for sustainable practices. +- Researchers & Scientists: Gain insights into the future of climate change, enabling further research on its impact and potential solutions. +- Educational Institutions: Provide data-driven resources for teaching about climate change and its effects on the environment. + + + + To view the Analysis πŸ‘‰ [Time series forecasting on Global Warming.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/EDA%20-%20Time%20Series%20Forecasting%20on%20Global%20Warming%20Trends.ipynb) + + To view the Datasets πŸ‘‰ [long format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/long_format_annual_surface_temp.csv) + [Wide format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/wide_format_annual_surface_temp.csv) + [Max_Min Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/Max_Min_Dataset.csv) From 53d548999910c2123eec3af93f3ec7b5f3d4848b Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sun, 20 Oct 2024 20:59:42 +0530 Subject: [PATCH 11/13] Update README.md --- .../README.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md b/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md index 28f13fa66..700335f9a 100644 --- a/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md +++ b/Data Analysis/Sentiment Analysis - Dow Jones (DJIA) Stock using News Headlines/README.md @@ -14,22 +14,30 @@ This project focuses on sentiment analysis, a technique used to determine the em ### Approach : - Data Cleaning & Preprocessing: Clean the data to remove noise (e.g., hashtags, URLs, special characters), and preprocess it by tokenizing, removing stopwords, and lemmatizing the text. + - Exploratory Data Analysis (EDA): Visualize the distribution of sentiments in the dataset. Identify common words and phrases associated with positive and negative sentiments. Analyze sentiment over time to observe trends. - Model Development: Use machine learning algorithms such as Naive Bayes, Multinomial Naive Bayes,Random Forest Classifier , Logistics Regression or deep learning techniques like LSTM to build a sentiment classification model. -- Sentiment Scoring: Assign sentiment scores to text to quantify the degree of positivity or negativity. + + - Model Development: Use machine learning algorithms such as Naive Bayes, Multinomial Naive Bayes,Random Forest Classifier , Logistics Regression or deep learning techniques like LSTM to build a sentiment classification model. + +- Sentiment Scoring: Assign sentiment scores to text to quantify the degree of positivity or negativity. + - Evaluation & Optimization: Evaluate model performance with precision metrics. ### Applications : Businesses: Use predictive insights to improve products, services, and customer engagement strategies. + Marketing Teams: Forecast the success of campaigns and adjust strategies based on predicted trends. + Social Media Monitoring: Track real-time sentiment and predict future public reactions to events, products, or announcements. -To view the Analysis πŸ‘‰ [Sentiment Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Sentiment%20Analysis.ipynb) -To view More charts in the Analysis πŸ‘‰ [Sentiment analysis charts](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/ChartsForBetterUnderstanding.ipynb) -To view the Dataset πŸ‘‰ [Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Headlines.csv) +## To view the Analysis πŸ‘‰ [Sentiment Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Sentiment%20Analysis.ipynb) + +## To view More charts in the Analysis πŸ‘‰ [Sentiment analysis charts](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/ChartsForBetterUnderstanding.ipynb) + +## To view the Dataset πŸ‘‰ [Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Sentiment%20Analysis%20-%20Dow%20Jones%20(DJIA)%20Stock%20using%20News%20Headlines/Stock%20Headlines.csv) From cd465cf03edf38c600bcae455a61805d383a61f5 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sun, 20 Oct 2024 21:00:59 +0530 Subject: [PATCH 12/13] Update README.md --- .../Time Series Forecasting on Global Warming/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Data Analysis/Time Series Forecasting on Global Warming/README.md b/Data Analysis/Time Series Forecasting on Global Warming/README.md index 62397230b..1be38bf8d 100644 --- a/Data Analysis/Time Series Forecasting on Global Warming/README.md +++ b/Data Analysis/Time Series Forecasting on Global Warming/README.md @@ -24,8 +24,8 @@ Compare model performance to select the best approach for accurate long-term for - To view the Analysis πŸ‘‰ [Time series forecasting on Global Warming.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/EDA%20-%20Time%20Series%20Forecasting%20on%20Global%20Warming%20Trends.ipynb) + ## To view the Analysis πŸ‘‰ [Time series forecasting on Global Warming.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/EDA%20-%20Time%20Series%20Forecasting%20on%20Global%20Warming%20Trends.ipynb) - To view the Datasets πŸ‘‰ [long format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/long_format_annual_surface_temp.csv) +## To view the Datasets πŸ‘‰ [long format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/long_format_annual_surface_temp.csv) [Wide format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/wide_format_annual_surface_temp.csv) [Max_Min Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/Max_Min_Dataset.csv) From c7a033887748c3ab7c1c426d5bdf63bce50bba88 Mon Sep 17 00:00:00 2001 From: Archita Paul <146704161+Archi20876@users.noreply.github.com> Date: Sun, 20 Oct 2024 21:02:06 +0530 Subject: [PATCH 13/13] Update README.md --- .../Time Series Forecasting on Global Warming/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Data Analysis/Time Series Forecasting on Global Warming/README.md b/Data Analysis/Time Series Forecasting on Global Warming/README.md index 1be38bf8d..602d65c54 100644 --- a/Data Analysis/Time Series Forecasting on Global Warming/README.md +++ b/Data Analysis/Time Series Forecasting on Global Warming/README.md @@ -27,5 +27,4 @@ Compare model performance to select the best approach for accurate long-term for ## To view the Analysis πŸ‘‰ [Time series forecasting on Global Warming.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/EDA%20-%20Time%20Series%20Forecasting%20on%20Global%20Warming%20Trends.ipynb) ## To view the Datasets πŸ‘‰ [long format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/long_format_annual_surface_temp.csv) - [Wide format Annual surface temp](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/wide_format_annual_surface_temp.csv) - [Max_Min Dataset](https://github.com/Archi20876/machine-learning-repos/blob/main/Data%20Analysis/Time%20Series%20Forecasting%20on%20Global%20Warming/Max_Min_Dataset.csv) +