<jupyter_start><jupyter_text>Explorative Data Analysis of Students Academic Score in three subjects: Math, Reading and Writing The dataset contains the score of students in three subjects (Math, Reading and Writing) together with some other information like gender, ethnic group, parent's level of education etc.Internet Link; https://www.kaggle.com/spscientist/students-performance-in-exams 1. Importing Pandas and Numpy Libraries<jupyter_code>import numpy as np
import pandas as pd<jupyter_output><empty_output><jupyter_text>2. Reading in and checking details of the data<jupyter_code>students=pd.read_csv("StudentsPerformance.csv")
students.head(2)
students.columns
for y in students.columns:
    print (y)
students.info()
students.describe()
students.shape
students.size
print("Students from Etnicity group E has the highest average score for all the three subjects: Math, Reading and Writing Score")
students.groupby("race/ethnicity").describe()
print ("Overall students with parents having M.Sc degrees have the highest averages score for all the three subjects: Math, Reading and Writing Score")
students.groupby("parental level of education").describe()
print("Females score higher averages in Reading and Writing with than the males who score better in Math")
students.groupby("gender", axis=0).describe()
print("Students with standard meals have the highest averages across the three subjects: Math, Reading and Writing score.")
students.groupby("lunch").describe()<jupyter_output>Students with standard meals have the highest averages across the three subjects: Math, Reading and Writing score.<jupyter_text>3. Importing visualization libraries: Seaborn and Matplotlib<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
print("All visualizations at a glance.")
sns.pairplot(data=students,hue='gender')
print ("Group C race has the highest number of students in the dataset")
plt.figure(figsize=(8,6), dpi=100)
plt.title("Count of each group of students in the dataset")
sns.set_style("darkgrid")
sns.countplot(students["race/ethnicity"],color="Blue", saturation=0.1)
print("Female students are slightly above the male students")
plt.title("Gender Count")
sns.countplot(students["gender"])
print("Parents with masters degree lowest in the dataset")
plt.figure(figsize=(8,4),dpi=100)
plt.title("Parents Level of Education")
sns.countplot(students["parental level of education"], color="Blue", saturation=0.1)
print("More standard lunch, almost twice that of free reduced")
plt.figure(figsize=(8,4), dpi=100)
plt.title("Count of Lunch Type")
sns.countplot(students["lunch"])
print ("Only 30% of the students completed the test preparation course")
plt.figure(figsize=(8,4), dpi=100)
plt.title("Test Preparation")
sns.countplot(students["test preparation course"])
print("Most students getting above 65 for math")
plt.xlabel("Scores")
plt.ylabel("Count")
plt.title("Math Score")
students["math score"].hist(bins=50, figsize=(10,6))
students["math score"].describe()
print("Most students getting close to 80 for reading")
plt.xlabel("Scores")
plt.ylabel("Count")
plt.title("Reading Score")
students["reading score"].hist(bins=50, figsize=(10,6))
students["reading score"].describe()
print("Most students getting close to 80 for writing")
plt.xlabel("Scores")
plt.ylabel("Count")
plt.title("Writing Score")
students["writing score"].hist(bins=50, figsize=(10,6))
students["writing score"].describe()

#Overall seems students do quite well in reading and writing than maths<jupyter_output>Most students getting close to 80 for writing<jupyter_text>4. Checking for Correlations in the dataset<jupyter_code>print("Very strong correlation between reading and writing score.")
plt.title("Correlation between subjects")
sns.heatmap(students.corr(), annot=True,cmap="coolwarm")<jupyter_output>Very strong correlation between reading and writing score.
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