Emotion recognition in text

Emotion Recognition In Text/Dataset/README.md

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+EMOTION RECOGNITION IN TEXT
+
+GOAL
+
+To develop a model that can analyze text data and classify the emotions expressed (e.g., happiness, sadness, anger).
+
+
+DATASET
+
+https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text
+
+DESCRIPTION
+
+To analyze the dataset of emotion detction from text and build,train the model on the basis of different features and variables.
+
+Visualization and EDA of different attributes:
+
+
+
+
+
+![sentiment distribution](https://github.com/abhisheks008/ML-Crate/assets/136368774/563e9d32-6c49-40a3-803a-419931a84f3a)
+
+
+
+
+
+
+
+
+
+
+
+
+
+WHAT I HAD DONE
+
+1.Load the dataset which contains about 40000 entires.
+
+2.Checked for missing values and cleaned the data accordingly.
+
+3.Analyzed the data, found insights and visualized them accordingly.
+
+4.Plotting distribution graphs to find corelations.
+
+5.Found detailed insights of different columns with target variable using plotting libraries.
+
+6.Trained the datasets by different models and saves their accuracies into a dataframe.
+
+
+MODELS USED
+
+
+Random forest classifier as it shows high accuracy,versatility and scalability.
+
+Gradient booster which is a strong algorithm for classification and regression problems
+
+XGBClassifier  help to improve machine-learning model's accuracy.
+
+
+LIBRARIES NEEDED
+
+Numpy
+
+Pandas
+
+Word cloud
+
+Matplotlib
+
+Seaborn
+
+Scikit-Learn
+
+Scipy
+
+Xgboost
+
+Tensorflow
+
+Keras
+
+
+VISUALIZATION
+
+INCLUSION OF IMAGES OF THE VISUALIZATION IS MUST (RESULT OF EDA).
+
+ACCURACIES
+
+Random forest classifier Score = 1.0
+
+Gradient booster Score = 1.0
+
+XGBClassifier Score = 0.25
+
+
+CONCLUSION
+
+Random forest classifier and Gradient booste rmodels show promising performance .
+
+XGBClassifier shows less accuracy
+
+
+YOUR NAME
+
+SRUJANA
+
+

Emotion Recognition In Text/README.md

@@ -0,0 +1,108 @@
+EMOTION RECOGNITION IN TEXT
+
+GOAL
+
+To develop a model that can analyze text data and classify the emotions expressed (e.g., happiness, sadness, anger).
+
+
+DATASET
+
+https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text
+
+DESCRIPTION
+
+To analyze the dataset of emotion detction from text and build,train the model on the basis of different features and variables.
+
+Visualization and EDA of different attributes:
+
+
+
+
+
+![sentiment distribution](https://github.com/abhisheks008/ML-Crate/assets/136368774/563e9d32-6c49-40a3-803a-419931a84f3a)
+
+
+
+
+
+
+
+
+
+
+
+
+
+WHAT I HAD DONE
+
+1.Load the dataset which contains about 40000 entires.
+
+2.Checked for missing values and cleaned the data accordingly.
+
+3.Analyzed the data, found insights and visualized them accordingly.
+
+4.Plotting distribution graphs to find corelations.
+
+5.Found detailed insights of different columns with target variable using plotting libraries.
+
+6.Trained the datasets by different models and saves their accuracies into a dataframe.
+
+
+MODELS USED
+
+
+Random forest classifier as it shows high accuracy,versatility and scalability.
+
+Gradient booster which is a strong algorithm for classification and regression problems
+
+XGBClassifier  help to improve machine-learning model's accuracy.
+
+
+LIBRARIES NEEDED
+
+Numpy
+
+Pandas
+
+Word cloud
+
+Matplotlib
+
+Seaborn
+
+Scikit-Learn
+
+Scipy
+
+Xgboost
+
+Tensorflow
+
+Keras
+
+
+VISUALIZATION
+
+INCLUSION OF IMAGES OF THE VISUALIZATION IS MUST (RESULT OF EDA).
+
+ACCURACIES
+
+Random forest classifier Score = 1.0
+
+Gradient booster Score = 1.0
+
+XGBClassifier Score = 0.25
+
+
+CONCLUSION
+
+Random forest classifier and Gradient booste rmodels show promising performance .
+
+XGBClassifier shows less accuracy
+
+
+YOUR NAME
+
+SRUJANA
+
+

Emotion Recognition In Text/requirements.txt

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+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2
+tensorflow==2.4.1
+keras==2.4.0