During our class session this week, we will discuss KNN as one of the straightforward algorithmsapplicable to classification problems. However, it's crucial to acknowledge the existence of numerousother classification algorithms. Among these alternatives, decision tree algorithms hold a prominentposition, and in this activity, we'll delve deeper into exploring its intricacies.Dataset Description:You are given a dataset containing information about playing tennis based on weather conditions. Thedataset consists of the following features: Temperature: Categorical feature representing the temperature $($Hot$,$ Mild, Cool$).$ Humidity: Categorical feature representing the humidity level $($High$,$ Normal$).$ Wind: Categorical feature representing the wind strength $($Strong$,$ Weak$).$ Outlook: Categorical feature representing the weather outlook $($Sunny$,$ Overcast, Rainy$).$ Play Tennis: Binary target variable indicating whether tennis was played $($Yes$)$ or not $($No$).$ID Temperature Humidity Wind Outlook Play Tennis$1$ Hot High Weak Sunny No$2$ Hot High Strong Sunny No$3$ Mild High Weak Overcast Yes$4$ Cool Normal Weak Rainy Yes$5$ Cool Normal Strong Rainy No$6$ Mild Normal Weak Rainy Yes$7$ Mild High Strong Sunny Yes$8$ Hot Normal Weak Sunny No$9$ Hot Normal Strong Rainy No$10$ Mild Normal Strong Overcast Yes$11$ Cool High Weak Overcast Yes$12$ Mild High Strong Overcast Yes$13$ Cool Normal Weak Sunny Yes$14$ Mild High Weak Rainy No