stu_perf_experiments_pearson_correlation.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Experiment for Pearson Correlation for more complex datasets\n\nDataset location:\nhttps://www.kaggle.com/spscientist/students-performance-in-exams"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\n\ndf = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head(50)\n\n# [lunch, [math_score, reading, writing], .9*.5]\n\n# Cutoff < .5","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.isna().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"corr_matrix = df.corr(method = 'pearson')\ncorr_matrix","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['gender'].unique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.groupby('race/ethnicity').count()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[(df['race/ethnicity'] == 'group A')]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Note: Possibly not fair to columns with many different types\n# Weakness in correlation is that outliers are used in calculation\n\n# Section to convert categories of type str to int. Allows correlation analysis to be done\n# Ex: gender male, female becomes 0,1\ndf['gender']=df['gender'].astype('category').cat.codes\ndf['race/ethnicity']=df['race/ethnicity'].astype('category').cat.codes\ndf['parental level of education']=df['parental level of education'].astype('category').cat.codes\ndf['lunch']=df['lunch'].astype('category').cat.codes\ndf['test preparation course']=df['test preparation course'].astype('category').cat.codes\n\ndf.corr()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}