In [2]:
import pandas as pd # to read csv file and xlsx file
import numpy as np # to perform numerical operations
from sklearn.model_selection import train_test_split #
from sklearn.tree import DecisionTreeClassifier, export_text
from sklearn.metrics import classification_report
In [3]:
# 1. Generate synthetic data
np.random.seed(42) # Ensure reproducibility
# Generate Temperature (random float values between 15 and 40)
temperature = np.random.uniform(15, 40, 100)
# Generate Humidity (random float values between 30 and 90)
humidity = np.random.uniform(30, 90, 100)
# Generate Target based on simple rules
target = ['Yes' if t > 25 and h > 50 else 'No' for t, h in zip(temperature, humidity)]
# Create DataFrame
data = pd.DataFrame({
'Temperature': temperature,
'Humidity': humidity,
'Target': target
})
#
In [4]:
print("Sample Dataset:")
print(data.head(50))
Sample Dataset:
Temperature Humidity Target
0 24.363503 31.885751 No
1 38.767858 68.184625 Yes
2 33.299849 48.861359 No
3 29.966462 60.514241 Yes
4 18.900466 84.453988 No
5 18.899863 44.957534 No
6 16.452090 54.622975 No
7 36.654404 75.333068 Yes
8 30.027875 43.727890 No
9 32.701814 34.618795 No
10 15.514612 47.385087 No
11 39.247746 39.673277 No
12 35.811066 85.781859 Yes
13 20.308478 78.487223 No
14 19.545624 68.004225 No
15 19.585113 82.287635 No
16 22.606056 78.220325 No
17 28.118911 41.194204 No
18 25.798625 83.553540 Yes
19 22.280729 62.360535 No
20 30.296322 78.446409 Yes
21 18.487347 83.765478 No
22 22.303616 49.080208 No
23 24.159046 36.603115 No
24 26.401750 43.676110 No
25 34.629399 55.626467 Yes
26 19.991845 79.080886 No
27 27.855861 81.643835 Yes
28 29.810364 30.417128 No
29 16.161260 60.644838 No
30 30.188621 55.044660 Yes
31 19.263103 43.326469 No
32 16.626290 37.191922 No
33 38.722138 50.256910 Yes
34 39.140801 86.574582 Yes
35 35.209934 49.392176 No
36 22.615344 61.127437 No
37 17.441803 72.181138 No
38 32.105826 51.817776 Yes
39 26.003812 88.306925 Yes
40 18.050956 87.746838 No
41 27.379423 45.106938 No
42 15.859713 59.834910 No
43 37.733010 48.052699 No
44 21.469500 47.090430 No
45 31.563057 32.213217 No
46 22.792777 66.573860 No
47 28.001701 60.160741 Yes
48 28.667757 33.088725 No
49 19.621361 46.718788 No
In [ ]:
# 2. Train a Decision Tree Classifier
# Prepare data for training
X = data[['Temperature', 'Humidity']]
y = data['Target']
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize Decision Tree Classifier
clf = DecisionTreeClassifier(random_state=42)
# Fit the model
clf.fit(X_train, y_train) # save this model
# Predict on the test set
y_pred = clf.predict(X_test)
In [7]:
# Evaluate the model
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Visualize the decision tree rules
tree_rules = export_text(clf, feature_names=['Temperature', 'Humidity'])
print("\nDecision Tree Rules:")
print(tree_rules)
Classification Report:
precision recall f1-score support
No 1.00 1.00 1.00 11
Yes 1.00 1.00 1.00 9
accuracy 1.00 20
macro avg 1.00 1.00 1.00 20
weighted avg 1.00 1.00 1.00 20
Decision Tree Rules:
|--- Temperature <= 25.20
| |--- class: No
|--- Temperature > 25.20
| |--- Humidity <= 49.87
| | |--- class: No
| |--- Humidity > 49.87
| | |--- class: Yes
In [8]:
X_test
Out[8]:
| Temperature | Humidity | |
|---|---|---|
| 83 | 16.588959 | 82.640361 |
| 53 | 37.370684 | 59.367166 |
| 70 | 34.306119 | 70.653862 |
| 45 | 31.563057 | 32.213217 |
| 44 | 21.469500 | 47.090430 |
| 39 | 26.003812 | 88.306925 |
| 22 | 22.303616 | 49.080208 |
| 80 | 36.577586 | 50.463981 |
| 10 | 15.514612 | 47.385087 |
| 0 | 24.363503 | 31.885751 |
| 18 | 25.798625 | 83.553540 |
| 30 | 30.188621 | 55.044660 |
| 73 | 35.386536 | 43.589747 |
| 33 | 38.722138 | 50.256910 |
| 90 | 17.989856 | 35.586166 |
| 4 | 18.900466 | 84.453988 |
| 76 | 34.281759 | 71.456264 |
| 77 | 16.851116 | 53.204121 |
| 12 | 35.811066 | 85.781859 |
| 31 | 19.263103 | 43.326469 |
In [19]:
sensor_data = pd.DataFrame({'Temperature': [100.588959], 'Humidity': [30]}) # Real time sensor data
clf.predict(sensor_data)[0]
Out[19]:
'No'
In [20]:
if clf.predict(sensor_data)[0]=="Yes":
print("Alert")
else:
print("No Alert")
No Alert
In [1]:
pip install pyserial
Defaulting to user installation because normal site-packages is not writeable Collecting pyserial Downloading pyserial-3.5-py2.py3-none-any.whl.metadata (1.6 kB) Downloading pyserial-3.5-py2.py3-none-any.whl (90 kB) Installing collected packages: pyserial Successfully installed pyserial-3.5 Note: you may need to restart the kernel to use updated packages.
In [ ]:
import serial
# Initialize Serial connection
arduino = serial.Serial(port='/dev/cu.usbserial-110', baudrate=9600, timeout=1) # Replace 'COM3' with your port
while True:
data = arduino.readline().decode('utf-8').strip()
if data:
try:
temperature, humidity = map(float, data.split(","))
print(f"Temperature: {temperature}°C, Humidity: {humidity}%")
except ValueError:
print("Invalid data received:", data)
Invalid data received: DHTxx test! Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F Invalid data received: Humidity: 49.00% Temperature: 20.70°C 69.26°F Heat index: 20.10°C 68.19°F
In [ ]: