Predictor.py

# -*- coding: utf-8 -*-
"""project1

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1JKalG1fiTa7i7JpKpz2907BgtJ2roAK2
"""

from google.colab import drive
drive.mount('/content/drive')

import pandas as pd
import matplotlib.pyplot as plt

# read CSV file into a pandas DataFrame
df = pd.read_csv("/content/drive/MyDrive/mini_project/pushup_count1.csv")

# plot the data as a scatter plot
df.plot(kind="scatter", x="Age", y="Push up counted", c="green")

# set plot title and axis labels
plt.title("Scatter Plot")
plt.xlabel("Age")
plt.ylabel("Push up counted")

# display plot
plt.show()

import pandas as pd
data = pd.read_csv("/content/drive/MyDrive/mini_project/pushup_count1.csv")


X = data.drop("Age", axis=1)
y = data["Push up counted"]

#Splitting
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

#Train
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)


from sklearn.metrics import accuracy_score
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Load the dataset from CSV file
data = pd.read_csv('/content/drive/MyDrive/mini_project/pushup_count1.csv')

# Extract the features (age) and labels (push-up count)
X = data.iloc[:, :-1].values
y = data.iloc[:, 1].values

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Train the linear regression model on the training set
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Predict the push-up counts for the test set
y_pred = regressor.predict(X_test)

# Evaluate the accuracy of the model using mean squared error
mse = mean_squared_error(y_test, y_pred)
print("Mean squared error:", mse)

# Use the trained model to predict the number of push-ups for a person of age 20
age = 14
pushups = regressor.predict([[age]])
print("A person of age", age, "can do", int(pushups), "push-ups.")

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Load the dataset from CSV file
data = pd.read_csv('/content/drive/MyDrive/mini_project/pushup_count1.csv')

# Extract the features (age) and labels (push-up count)
X = data.iloc[:, :-1].values
y = data.iloc[:, 1].values

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Train the linear regression model on the training set
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# Predict the push-up counts for the test set
y_pred = regressor.predict(X_test)

# Evaluate the accuracy of the model using mean squared error
mse = mean_squared_error(y_test, y_pred)
print("Mean squared error:", mse)

# Use the trained model to predict the number of push-ups for a person of age 20
age = 18
pushups = regressor.predict([[age]])
print("A person of age", age, "can do", int(pushups), "push-ups.")

if(age>=10 and age<=15):
  print("Standard number is: 8")
elif(age>=16 and age<=20):
  print("Standard number is: 12")
elif(age>=21 and age<=25):
  print("Standard number is: 20")
elif(age>=26 and age<=30):
  print("Standard number is: 24")
elif(age>=31 and age<=40):
  print("Standard number is: 28")
else:
  print(1)

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Load the CSV file into a Pandas DataFrame
df = pd.read_csv('/content/drive/MyDrive/mini_project/pushup_count1.csv')

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('Push up counted', axis=1), df['Age'], test_size=0.2, random_state=42)

# Train a random forest classifier on the training set
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = rf.predict(X_test)

# Calculate accuracy
accuracy = 100*accuracy_score(y_test, y_pred)

print("Accuracy:", accuracy)

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score


data = pd.read_csv("/content/drive/MyDrive/mini_project/pushup_count1.csv")


X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

regressor=LinearRegression()
regressor.fit(X_train, y_train)


y_pred = regressor.predict(X_test)


mse = mean_squared_error(y_test, y_pred)
print("Mean squared error:", mse)


age = int(input('enter age='))

pushups = regressor.predict([[age]])
print("A person of age", age, "can do", int(pushups), "push-ups.")