From b26603f1fe755f9f43f997e6e03493e460420e74 Mon Sep 17 00:00:00 2001
From: Rishabh Singh Chauhan <143814425+Rsc2414@users.noreply.github.com>
Date: Sat, 19 Jul 2025 20:58:01 +0530
Subject: [PATCH] Update 001. Laptop Battery Life.py

---
 .../001. Laptop Battery Life.py               | 26 ++++++++++++++-----
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/Artificial Intelligence/Statistics and Machine Learning/001. Laptop Battery Life.py b/Artificial Intelligence/Statistics and Machine Learning/001. Laptop Battery Life.py
index 3c62a74..c0ce2ec 100644
--- a/Artificial Intelligence/Statistics and Machine Learning/001. Laptop Battery Life.py	
+++ b/Artificial Intelligence/Statistics and Machine Learning/001. Laptop Battery Life.py	
@@ -1,15 +1,29 @@
 # Problem: https://www.hackerrank.com/challenges/battery/problem
 # Score: 10
-
+# By @Rsc2414
 
 import pandas as pd
 from sklearn.linear_model import LinearRegression
 
+# Read the input time charged
+time_charged = float(input())
 
-timeCharged = float(input())
+# Load the training data from the file
 data = pd.read_csv('trainingdata.txt', names=['charged', 'lasted'])
-train = data[data['lasted'] < 8]
+
+# Use only data points where battery lasted less than 8 hours (as per problem note)
+filtered_data = data[data['lasted'] < 8]
+
+# Prepare the training input (X) and output (y)
+X = filtered_data['charged'].values.reshape(-1, 1)
+y = filtered_data['lasted'].values.reshape(-1, 1)
+
+# Create and train the linear regression model
 model = LinearRegression()
-model.fit(train['charged'].values.reshape(-1, 1), train['lasted'].values.reshape(-1, 1))
-ans = model.predict([[timeCharged]])
-print(min(ans[0][0], 8))
+model.fit(X, y)
+
+# Predict battery life for the given charging time
+predicted = model.predict([[time_charged]])
+
+# Print the predicted time, but cap it at 8 hours
+print(min(predicted[0][0], 8))