#845 Added fifa dataset

Niketkumardheeryan · Jun 23, 2024 · 356650f · 356650f
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diff --git a/FIFA 19 Dataset Analysis/DataSet/Data.csv b/FIFA 19 Dataset Analysis/DataSet/Data.csv
diff --git a/FIFA 19 Dataset Analysis/DataSet/README.md b/FIFA 19 Dataset Analysis/DataSet/README.md
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+Dataset Link: https://www.kaggle.com/karangadiya/fifa19
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diff --git a/FIFA 19 Dataset Analysis/Model/FIFA_19_Dataset_Analysis.ipynb b/FIFA 19 Dataset Analysis/Model/FIFA_19_Dataset_Analysis.ipynb
diff --git a/FIFA 19 Dataset Analysis/Model/README.md b/FIFA 19 Dataset Analysis/Model/README.md
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+# PROJECT TITLE
+
+FIFA 19 Dataset Analysis
+
+## GOAL
+
+**Aim -** Analyze different aspects of the dataset and provide the pattern of the dataset from the visualization.
+
+## DATASET
+
+https://www.kaggle.com/karangadiya/fifa19
+
+## DESCRIPTION
+
+This is a regression problem where we analyze the FiFA 19 Data and predict the value. We use Linear Regression, Decision Tree  and Random Forest Regressor.
+
+## WHAT I HAD DONE
+
+1. Perfromed exploratory data analysis (EDA) on the given dataset
+2. It starts with loading the dataset and viewing the top 5 rows
+3. We calculate statistical data in the dataset
+4. Then comes finding correlation between the features and also finding statistical values related to the dataset
+5. Data visualization is done with libraries such as matplotlib and seaborn
+6. Finally 3 different algorithms are used to find the best algorithm 
+7. Also accuracy score of each algorithm is calculated for comparison purpose with other algorithms
+
+## DATA VISUALIZATION
+
+![image](https://user-images.githubusercontent.com/78292851/154267611-e086ea34-8c1a-4d24-98df-9c5a2df7d4ed.png)
+
+![image](https://user-images.githubusercontent.com/78292851/154267654-ce055236-da81-4c77-ab20-e1bab346746a.png)
+
+![image](https://user-images.githubusercontent.com/78292851/154267682-5b640296-d9ec-4815-bc96-5d7db0ab9c7e.png)
+
+
+
+## MODELS USED
+
+1. Linear Regression= simplest and most common algorithm used for classification problems
+2. Decision Tree Regression
+3. Random Forest Regressor
+
+
+## LIBRARIES NEEDED
+
+1. Numpy
+2. Pandas
+3. Matplotlib
+4. Seaborn
+5. Scikit-Learn
+
+## ACCURACIES
+
+1. Linear Regression Score = 64.99%
+2. Random Forest Regressor = 99.04%
+3. Decision Tree  = 99.86%
+
+## CONCLUSION
+
+We can conclude that Decision Tree gives the most accurate results specifically for this problem statement.
+
+## Authors 
+
+- Created by [@Priyankesh](https://github.com/priyankeshh), GSSoC 2024
diff --git a/FIFA 19 Dataset Analysis/requirements.txt b/FIFA 19 Dataset Analysis/requirements.txt
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+matplotlib==3.9.0
+seaborn==0.13.2
+numpy==1.26.4
+pandas==2.2.2
+scikit_learn==1.5.0