import pandas as pd import matplotlib.pyplot as plt

df = pd.read_csv("test_scores.csv")

df

df["school"].value_counts().plot(kind="barh")

my_plot = df[["school_setting", "posttest"]].boxplot(by="school_setting")

my_plot = df[["school_type", "posttest"]].boxplot(by="school_type")

my_plot = df[["teaching_method", "posttest"]].boxplot(by="teaching_method")

df[["n_student", "posttest"]].plot.scatter(x="n_student", y="posttest")

my_plot = df[["lunch", "posttest"]].boxplot(by="lunch")

df.groupby(["school_setting"])["lunch"].value_counts(normalize=True)

df[["pretest", "posttest"]].plot.scatter(x="pretest", y="posttest")

df.groupby(["school_setting"])["gender"].value_counts(normalize=True)

df["school_setting"].value_counts().plot.bar()

df.groupby(["teaching_method"])["lunch"].value_counts(normalize=True)

df.groupby(["school_setting"])["teaching_method"].value_counts(normalize=True)

df.groupby(["school_type"])["teaching_method"].value_counts(normalize=True)

import statsmodels.api as sm from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.compose import ColumnTransformer

X = df[["school_setting", "school_type", "teaching_method", "n_student", "gender", "lunch"]] y = df[["posttest"]]

one_hot_columns = ["school_setting", "school_type", "teaching_method", "gender", "lunch"] scaling_columns = ["n_student"] transformation_pipeline = ColumnTransformer([ ("one_hot_encoder", OneHotEncoder(), one_hot_columns), ("standard_scaler", StandardScaler(), scaling_columns) ]) X_transformed_raw = transformation_pipeline.fit_transform(X)

X_transformed = pd.DataFrame(X_transformed_raw, columns=[ "Is_Rural", "Is_Suburban", "Is_Urban", "Is_Non_Public", "Is_Public", "Is_Non_Standard_Teaching", "Is_Standard_Teaching", "Is_Female", "Is_Male", "Is_Not_Qualify_Lunch", "Is_Qualify_Lunch", "Num_Students" ])

X_transformed

df