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Children's Motor Performance

📖 Background

Measuring the physical abilities of children is helpful for understanding growth and development, as well as identifying gifted individuals by sports talent scouts. A common measure for physical abilities is the Motor Performance Index.

An athletics talent scout has hired you to find insights in a dataset to assist their search for the next generation of track and field stars.

💾 The data

The dataset is a slightly cleaned version of a dataset described in the article Kids motor performances datasets from the Data in Brief journal.

The dataset consists of a single CSV file, data/motor-performance.csv.

Each row represents a seven year old Malaysian child.

Four properties of motor skills were recorded.

  • POWER (cm): Distance of a two-footed standing jump.
  • SPEED (sec): Time taken to sprint 20m.
  • FLEXIBILITY (cm): Distance reached forward in a sitting position.
  • COORDINATION (no.): Number of catches of a ball, out of ten.

Full details of these metrics are described in sections 2.2 to 2.5 of the linked article.

Attributes of the children are included.

  • STATE: The Malaysian state where the child resides.
  • RESIDENTIAL: Whether the child lives in a rural or urban area.
  • GENDER: The child's gender, Female or Male.
  • AGE: The child's age in years.
  • WEIGHT (kg): The child's bodyweight in kg.
  • HEIGHT (CM): The child's height in cm.
  • BMI (kg/m2): The child's body mass index (weight in kg divided by height in meters squared).
  • CLASS (BMI): Categorization of the BMI: "SEVERE THINNESS", "THINNESS", "NORMAL", "OVERWEIGHT", "OBESITY".
import pandas as pd

motor_performance = pd.read_csv("data/motor-performance.csv")
motor_performance

💪 Challenge

Explore the dataset to understand how the attributes of the children affect the motor skills, and the relationship between the four motor skills. Your published notebook should contain a short report on the motor skills, including summary statistics, visualizations, statistical models, and text describing any insights you found.

IMPORTING LIBRARIES

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

EXPLORATORY DATA ANALYSIS

df = motor_performance
df.shape
# shape of the dataset
df.head()
# to view first 5 records
df.tail()
# to view last 5 records
df.info()
#info regarding dataset
df.describe()
# statistical description of dataset
df.corr()
#correlation between the attributes and the motor skills
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