ML Projects

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

#include<stdio.h>

#include<stdlib.h>

#include<time.h>

int main(){

int number, guess, nguesses=1;

srand(time(0));

number = rand()%100 + 1; // Generates a random number between 1 and 100

// printf("The number is %d\n", number);

// Keep running the loop until the number is guessed

do{

printf("Guess the number between 1 to 100\n");

scanf("%d", &guess);

if(guess>number){

printf("Lower number please!\n");

}

else if(guess<number){

printf("Higher number please!\n");

}

else{

printf("You guessed it in %d attempts\n", nguesses);

}

nguesses++;

}while(guess!=number);

return 0;

}

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using
Machine Learning

1. Importing Libraries and Loading the Data After the .csv file 'games.csv' has been copied to the current directory, we can import the data as a Pandas DataFrame. As a DataFrame, we will be able to easily explore the type, amount, and distribution of data. Furthermore, using a correlation matrix, we can explore the relationships between parameters. This is an important step in determining the type of machine learning algorithm to utilize.

import sys

import pandas

import matplotlib

import seaborn

import sklearn

# Read in the data.

games = pandas.read_csv("games.csv")

# Print the names of the columns in games.

print(games.columns)

print(games.shape)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

# games[games["average_rating"] == 0]

# Print the first row of all the games with zero scores.

# The .iloc method on dataframes allows us to index by position.

print(games[games["average_rating"] == 0].iloc[0])

# Print the first row of all the games with scores greater than 0.

print(games[games["average_rating"] > 0].iloc[0])

# Remove any rows without user reviews.

games = games[games["users_rated"] > 0]

# Remove any rows with missing values.

games = games.dropna(axis=0)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

#correlation matrix

corrmat = games.corr()

fig = plt.figure(figsize = (12, 9))

sns.heatmap(corrmat, vmax=.8, square=True);

plt.show()

# Get all the columns from the dataframe.

columns = games.columns.tolist()

# Filter the columns to remove ones we don't want.

columns = [c for c in columns if c not in ["bayes_average_rating", "average_rating", "type", "name", "id"]]

# Store the variable we'll be predicting on.

target = "average_rating"

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

# Import a convenience function to split the sets.

from sklearn.cross_validation import train_test_split

# Generate the training set. Set random_state to be able to replicate results.

train = games.sample(frac=0.8, random_state=1)

# Select anything not in the training set and put it in the testing set.

test = games.loc[~games.index.isin(train.index)]

# Print the shapes of both sets.

print(train.shape)

print(test.shape)

# Import the linear regression model.

from sklearn.linear_model import LinearRegression

# Initialize the model class.

model = LinearRegression()

# Fit the model to the training data.

model.fit(train[columns], train[target])

# Import the scikit-learn function to compute error.

from sklearn.metrics import mean_squared_error

# Generate our predictions for the test set.

predictions = model.predict(test[columns])

# Compute error between our test predictions and the actual values.

mean_squared_error(predictions, test[target])

# Import the random forest model.

from sklearn.ensemble import RandomForestRegressor

# Initialize the model with some parameters.

model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10, random_state=1)

# Fit the model to the data.

model.fit(train[columns], train[target])

# Make predictions.

predictions = model.predict(test[columns])

# Compute the error.

mean_squared_error(predictions, test[target])

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

We have provided lines of code and images for example for you to understand you better.

We will use Pycharm IDE as it's easy and convenient to use.

Pycharm

We will start by importing important libraries

import pyttsx3 # pip install pyttsx3

import datetime # It's an prebuilt library

import speech_recognition as sr # pip install SpeechRecognition

import wikipedia

import smtplib

import webbrowser as wb

import psutil

import pyjokes

import os

import pyautogui

import json

from urllib.request import urlopen

import wolframalpha

import time

engine = pyttsx3.init()

wolframalpha_app_id = '99LHQ4-3LA69J466R'

def speak(audio):

engine.say(audio)

engine.runAndWait()

def time_():

Time = datetime.datetime.now().strftime("%I:%M:%S") # for 12 hours clock

speak("The current time is")

speak(Time)

def date_():

year = datetime.datetime.now().year

month = datetime.datetime.now().month

date = datetime.datetime.now().day

speak("The current date is")

speak(date)

speak(month)

speak(year)

def wishme():

speak("Welcome back Yash! Hope you have a good day")

time_()

date_()

# Greetings

hour = datetime.datetime.now().hour

if hour >= 6 and hour < 12:

speak("Good Morning Yash!")

elif hour >= 12 and hour < 18:

speak("Good Afternoon Yash!")

elif hour >= 18 and hour < 24:

speak("Good Evening Yash!")

else:

speak("Good Night Yash!")

speak("Jravis at your service. Please tell me Yash how can i help you today?")

def TakeCommand():

r = sr.Recognizer()

with sr.Microphone() as source:

print("Listening....")

r.pause_threshold = 1

audio = r.listen(source)

try:

print("Recognizing....")

query = r.recognize_google(audio, language='eng-in')

print(query)

except Exception as e:

print(e)

print("Say that again please....")

return "None"

return query

def sendEmail(to, content):

server = smtplib.SMTP('smtp.gmail.com', 587)

server.ehlo()

server.starttls()

server.login('yashv.088@gmail.com', 'YashMech2002')

server.send('yashv.088@gmail.com', to, content)

server.close()

def screenshot():

img = pyautogui.screenshot

img.save('C:/Users/91916/Pictures/Screenshots/screenshot.png')

def cpu():

usage = str(psutil.cpu_percent())

speak('Yash! CPU is at'+usage)

battery = psutil.sensors_battery()

speak('Yash! Battery is at')

speak(battery.percent)

def joke():

speak(pyjokes.get_joke())

if __name__ == "__main__":

wishme()

while True:

query = TakeCommand().lower()

if 'time' in query:

time_()

elif 'date' in query:

date_()

elif 'wikipedia' in query:

speak("Searching....")

query = query.replace('wikipedia', '')

result = wikipedia.summary(query, sentences=5)

speak('According to wikipedia..')

print(result)

speak(result)

elif 'send email' in query:

try:

speak("What should I say?")

content = TakeCommand()

speak("Who is the Reciever?")

reciever = input("Enter Reciever's email :")

sendEmail(to,content)

speak('Email has been saved')

except Exception as e:

print(e)

speak("Unable to send Email.")

elif 'search in chrome' in query:

speak('What should i search?')

chromepath = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'

search = TakeCommand().lower()

wb.get(chromepath).open_new_tab(search+'.com')

elif 'search youtube' in query:

speak('sure Yash!')

search_Term = TakeCommand().lower()

speak("Here we go to You Tube!")

wb.open('https://www.youtube.com/results?search_query='+search_Term)

elif 'search google' in query:

speak('What should i search?')

search_Term = TakeCommand().lower()

speak('Searching')

wb.open('https://www.google.com/search?q='+search_Term)

elif 'cpu' in query:

cpu()

elif 'joke' in query:

joke()

elif 'go offline' in query:

speak('as you said going offline Yash!')

quit()

elif 'word' in query:

speak('Opening MS word....')

ms_word = 'C:/Program Files/Microsoft Office/root/Office16/WINWORD.EXE'

os.startfile(ms_word)

elif 'excel' in query:

speak('Opening MS Excel...')

ms_exc = 'C:/Program Files/Microsoft Office/root/Office16/EXCEL.EXE'

os.startfile(ms_exc)

elif 'powerpoint' in query:

speak('Opening MS power point')

ms_pp = 'C:/Program Files/Microsoft Office/root/Office16/POWERPNT.EXE'

os.startfile(ms_pp)

elif 'wpotify' in query:

speak('Opening Spotify..')

spo = 'C:/Users/91916/AppData/Roaming/Spotify/Spotify'

os.startfile(spo)

elif 'whatsApp' in query:

speak('Opening WhatsApp')

wha = 'C:/Users/91916/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/WhatsApp/WhatsApp'

os.startfile(wha)

elif 'write a note' in query:

speak("What should i write Yash!..")

notes = TakeCommand()

file = open('notes.txt','w')

speak("Yash! Should i include date and time?")

ans = TakeCommand()

if 'yes' in ans or 'sure' in ans:

strTime = datetime.datetime.now().strftime("%H:%M:%S")

file.write(strTime)

file.write(':-')

file.write(notes)

speak("Done taking notes Yash!")

else:

file.write(notes)

elif 'show note' in query:

speak('showing notes..')

file = open("notes.txt",'r')

print(file.read())

speak(file.read())

elif 'screenshot' in query:

screenshot()

elif 'remember that' in query:

speak('What should i remember?')

memory = TakeCommand()

speak("You asked me to remember that"+memory)

remember = open('memory.txt','w')

remember.write(memory)

remember.close()

elif 'do you remember anything' in query:

remember = open('memory.txt','r')

speak('you asked me to remember that'+remember.read())

elif 'where is' in query:

query = query.replace("where is","")

location = query

speak("User said to locate"+location)

wb.open_new_tab("https://www.google.com/maps/place/"+location)

elif 'news' in query:

try:

jsonObj = urlopen("https://newsapi.org/v2/everything?domains=techcrunch.com,thenextweb.com&apiKey=fded868197784840843d5339f0241224")

data = json.load(jsonObj)

i = 1

speak("Here are some top Headlines from thr tech industry")

print("===========TOP HEADLINES============"+"\n")

for item in data['articles']:

print(str(i)+'. '+item['title']+'\n')

print(item['description']+'\n')

speak(item['title'])

i += 1

except Exception as e:

print(str(e))

elif 'calculate' in query:

client = wolframalpha.Client(wolframalpha_app_id)

indx = query.lower().split().index('calculate')

query = query.split()[indx + 1:]

res = client.query(''.join(query))

answer = next(res.results).text

print('The Answer is :'+answer)

speak('The Answer is '+answer)

elif 'what is' in query or 'who is' in query:

client = wolframalpha.Client(wolframalpha_app_id)

res = client.query()

try:

print(next(res.results).text)

speak(next(res.results).text)

except StopIteration:

print("No Results")

elif 'stop listening' in query:

speak("For how many seconds you want me to stop listening?")

ans = int(TakeCommand())

time.sleep(ans)

print(ans)

elif 'restart' in query:

os.system("shutdown /r /t 1")

elif 'shutdown' in query:

os.system("shutdown /s /t 1")

elif 'tell about me' in query:

speak("Your name is Yash you are studying mechanical engineering and you are interested in machine learning")

elif 'play music' in query:

songs_dir = 'Y:/Music'

music = os.listdir(songs_dir)

speak("Here's your fav song")

os.startfile(os.path.join(songs_dir, music[3]))

This is the code for our AI Assistant we have covered a lot of things in it we hope you all will make your own.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

#include<stdio.h>

#include<stdlib.h>

#include<time.h>

int main(){

int number, guess, nguesses=1;

srand(time(0));

number = rand()%100 + 1; // Generates a random number between 1 and 100

// printf("The number is %d\n", number);

// Keep running the loop until the number is guessed

do{

printf("Guess the number between 1 to 100\n");

scanf("%d", &guess);

if(guess>number){

printf("Lower number please!\n");

}

else if(guess<number){

printf("Higher number please!\n");

}

else{

printf("You guessed it in %d attempts\n", nguesses);

}

nguesses++;

}while(guess!=number);

return 0;

}

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using
Machine Learning

1. Importing Libraries and Loading the Data After the .csv file 'games.csv' has been copied to the current directory, we can import the data as a Pandas DataFrame. As a DataFrame, we will be able to easily explore the type, amount, and distribution of data. Furthermore, using a correlation matrix, we can explore the relationships between parameters. This is an important step in determining the type of machine learning algorithm to utilize.

import sys

import pandas

import matplotlib

import seaborn

import sklearn

# Read in the data.

games = pandas.read_csv("games.csv")

# Print the names of the columns in games.

print(games.columns)

print(games.shape)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

# games[games["average_rating"] == 0]

# Print the first row of all the games with zero scores.

# The .iloc method on dataframes allows us to index by position.

print(games[games["average_rating"] == 0].iloc[0])

# Print the first row of all the games with scores greater than 0.

print(games[games["average_rating"] > 0].iloc[0])

# Remove any rows without user reviews.

games = games[games["users_rated"] > 0]

# Remove any rows with missing values.

games = games.dropna(axis=0)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

#correlation matrix

corrmat = games.corr()

fig = plt.figure(figsize = (12, 9))

sns.heatmap(corrmat, vmax=.8, square=True);

plt.show()

# Get all the columns from the dataframe.

columns = games.columns.tolist()

# Filter the columns to remove ones we don't want.

columns = [c for c in columns if c not in ["bayes_average_rating", "average_rating", "type", "name", "id"]]

# Store the variable we'll be predicting on.

target = "average_rating"

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

# Import a convenience function to split the sets.

from sklearn.cross_validation import train_test_split

# Generate the training set. Set random_state to be able to replicate results.

train = games.sample(frac=0.8, random_state=1)

# Select anything not in the training set and put it in the testing set.

test = games.loc[~games.index.isin(train.index)]

# Print the shapes of both sets.

print(train.shape)

print(test.shape)

# Import the linear regression model.

from sklearn.linear_model import LinearRegression

# Initialize the model class.

model = LinearRegression()

# Fit the model to the training data.

model.fit(train[columns], train[target])

# Import the scikit-learn function to compute error.

from sklearn.metrics import mean_squared_error

# Generate our predictions for the test set.

predictions = model.predict(test[columns])

# Compute error between our test predictions and the actual values.

mean_squared_error(predictions, test[target])

# Import the random forest model.

from sklearn.ensemble import RandomForestRegressor

# Initialize the model with some parameters.

model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10, random_state=1)

# Fit the model to the data.

model.fit(train[columns], train[target])

# Make predictions.

predictions = model.predict(test[columns])

# Compute the error.

mean_squared_error(predictions, test[target])

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

We have provided lines of code and images for example for you to understand you better.

We will use Pycharm IDE as it's easy and convenient to use.

Pycharm

We will start by importing important libraries

import pyttsx3 # pip install pyttsx3

import datetime # It's an prebuilt library

import speech_recognition as sr # pip install SpeechRecognition

import wikipedia

import smtplib

import webbrowser as wb

import psutil

import pyjokes

import os

import pyautogui

import json

from urllib.request import urlopen

import wolframalpha

import time

engine = pyttsx3.init()

wolframalpha_app_id = '99LHQ4-3LA69J466R'

def speak(audio):

engine.say(audio)

engine.runAndWait()

def time_():

Time = datetime.datetime.now().strftime("%I:%M:%S") # for 12 hours clock

speak("The current time is")

speak(Time)

def date_():

year = datetime.datetime.now().year

month = datetime.datetime.now().month

date = datetime.datetime.now().day

speak("The current date is")

speak(date)

speak(month)

speak(year)

def wishme():

speak("Welcome back Yash! Hope you have a good day")

time_()

date_()

# Greetings

hour = datetime.datetime.now().hour

if hour >= 6 and hour < 12:

speak("Good Morning Yash!")

elif hour >= 12 and hour < 18:

speak("Good Afternoon Yash!")

elif hour >= 18 and hour < 24:

speak("Good Evening Yash!")

else:

speak("Good Night Yash!")

speak("Jravis at your service. Please tell me Yash how can i help you today?")

def TakeCommand():

r = sr.Recognizer()

with sr.Microphone() as source:

print("Listening....")

r.pause_threshold = 1

audio = r.listen(source)

try:

print("Recognizing....")

query = r.recognize_google(audio, language='eng-in')

print(query)

except Exception as e:

print(e)

print("Say that again please....")

return "None"

return query

def sendEmail(to, content):

server = smtplib.SMTP('smtp.gmail.com', 587)

server.ehlo()

server.starttls()

server.login('yashv.088@gmail.com', 'YashMech2002')

server.send('yashv.088@gmail.com', to, content)

server.close()

def screenshot():

img = pyautogui.screenshot

img.save('C:/Users/91916/Pictures/Screenshots/screenshot.png')

def cpu():

usage = str(psutil.cpu_percent())

speak('Yash! CPU is at'+usage)

battery = psutil.sensors_battery()

speak('Yash! Battery is at')

speak(battery.percent)

def joke():

speak(pyjokes.get_joke())

if __name__ == "__main__":

wishme()

while True:

query = TakeCommand().lower()

if 'time' in query:

time_()

elif 'date' in query:

date_()

elif 'wikipedia' in query:

speak("Searching....")

query = query.replace('wikipedia', '')

result = wikipedia.summary(query, sentences=5)

speak('According to wikipedia..')

print(result)

speak(result)

elif 'send email' in query:

try:

speak("What should I say?")

content = TakeCommand()

speak("Who is the Reciever?")

reciever = input("Enter Reciever's email :")

sendEmail(to,content)

speak('Email has been saved')

except Exception as e:

print(e)

speak("Unable to send Email.")

elif 'search in chrome' in query:

speak('What should i search?')

chromepath = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'

search = TakeCommand().lower()

wb.get(chromepath).open_new_tab(search+'.com')

elif 'search youtube' in query:

speak('sure Yash!')

search_Term = TakeCommand().lower()

speak("Here we go to You Tube!")

wb.open('https://www.youtube.com/results?search_query='+search_Term)

elif 'search google' in query:

speak('What should i search?')

search_Term = TakeCommand().lower()

speak('Searching')

wb.open('https://www.google.com/search?q='+search_Term)

elif 'cpu' in query:

cpu()

elif 'joke' in query:

joke()

elif 'go offline' in query:

speak('as you said going offline Yash!')

quit()

elif 'word' in query:

speak('Opening MS word....')

ms_word = 'C:/Program Files/Microsoft Office/root/Office16/WINWORD.EXE'

os.startfile(ms_word)

elif 'excel' in query:

speak('Opening MS Excel...')

ms_exc = 'C:/Program Files/Microsoft Office/root/Office16/EXCEL.EXE'

os.startfile(ms_exc)

elif 'powerpoint' in query:

speak('Opening MS power point')

ms_pp = 'C:/Program Files/Microsoft Office/root/Office16/POWERPNT.EXE'

os.startfile(ms_pp)

elif 'wpotify' in query:

speak('Opening Spotify..')

spo = 'C:/Users/91916/AppData/Roaming/Spotify/Spotify'

os.startfile(spo)

elif 'whatsApp' in query:

speak('Opening WhatsApp')

wha = 'C:/Users/91916/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/WhatsApp/WhatsApp'

os.startfile(wha)

elif 'write a note' in query:

speak("What should i write Yash!..")

notes = TakeCommand()

file = open('notes.txt','w')

speak("Yash! Should i include date and time?")

ans = TakeCommand()

if 'yes' in ans or 'sure' in ans:

strTime = datetime.datetime.now().strftime("%H:%M:%S")

file.write(strTime)

file.write(':-')

file.write(notes)

speak("Done taking notes Yash!")

else:

file.write(notes)

elif 'show note' in query:

speak('showing notes..')

file = open("notes.txt",'r')

print(file.read())

speak(file.read())

elif 'screenshot' in query:

screenshot()

elif 'remember that' in query:

speak('What should i remember?')

memory = TakeCommand()

speak("You asked me to remember that"+memory)

remember = open('memory.txt','w')

remember.write(memory)

remember.close()

elif 'do you remember anything' in query:

remember = open('memory.txt','r')

speak('you asked me to remember that'+remember.read())

elif 'where is' in query:

query = query.replace("where is","")

location = query

speak("User said to locate"+location)

wb.open_new_tab("https://www.google.com/maps/place/"+location)

elif 'news' in query:

try:

jsonObj = urlopen("https://newsapi.org/v2/everything?domains=techcrunch.com,thenextweb.com&apiKey=fded868197784840843d5339f0241224")

data = json.load(jsonObj)

i = 1

speak("Here are some top Headlines from thr tech industry")

print("===========TOP HEADLINES============"+"\n")

for item in data['articles']:

print(str(i)+'. '+item['title']+'\n')

print(item['description']+'\n')

speak(item['title'])

i += 1

except Exception as e:

print(str(e))

elif 'calculate' in query:

client = wolframalpha.Client(wolframalpha_app_id)

indx = query.lower().split().index('calculate')

query = query.split()[indx + 1:]

res = client.query(''.join(query))

answer = next(res.results).text

print('The Answer is :'+answer)

speak('The Answer is '+answer)

elif 'what is' in query or 'who is' in query:

client = wolframalpha.Client(wolframalpha_app_id)

res = client.query()

try:

print(next(res.results).text)

speak(next(res.results).text)

except StopIteration:

print("No Results")

elif 'stop listening' in query:

speak("For how many seconds you want me to stop listening?")

ans = int(TakeCommand())

time.sleep(ans)

print(ans)

elif 'restart' in query:

os.system("shutdown /r /t 1")

elif 'shutdown' in query:

os.system("shutdown /s /t 1")

elif 'tell about me' in query:

speak("Your name is Yash you are studying mechanical engineering and you are interested in machine learning")

elif 'play music' in query:

songs_dir = 'Y:/Music'

music = os.listdir(songs_dir)

speak("Here's your fav song")

os.startfile(os.path.join(songs_dir, music[3]))

This is the code for our AI Assistant we have covered a lot of things in it we hope you all will make your own.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

#include<stdio.h>

#include<stdlib.h>

#include<time.h>

int main(){

int number, guess, nguesses=1;

srand(time(0));

number = rand()%100 + 1; // Generates a random number between 1 and 100

// printf("The number is %d\n", number);

// Keep running the loop until the number is guessed

do{

printf("Guess the number between 1 to 100\n");

scanf("%d", &guess);

if(guess>number){

printf("Lower number please!\n");

}

else if(guess<number){

printf("Higher number please!\n");

}

else{

printf("You guessed it in %d attempts\n", nguesses);

}

nguesses++;

}while(guess!=number);

return 0;

}

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using
Machine Learning

1. Importing Libraries and Loading the Data After the .csv file 'games.csv' has been copied to the current directory, we can import the data as a Pandas DataFrame. As a DataFrame, we will be able to easily explore the type, amount, and distribution of data. Furthermore, using a correlation matrix, we can explore the relationships between parameters. This is an important step in determining the type of machine learning algorithm to utilize.

import sys

import pandas

import matplotlib

import seaborn

import sklearn

# Read in the data.

games = pandas.read_csv("games.csv")

# Print the names of the columns in games.

print(games.columns)

print(games.shape)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

# games[games["average_rating"] == 0]

# Print the first row of all the games with zero scores.

# The .iloc method on dataframes allows us to index by position.

print(games[games["average_rating"] == 0].iloc[0])

# Print the first row of all the games with scores greater than 0.

print(games[games["average_rating"] > 0].iloc[0])

# Remove any rows without user reviews.

games = games[games["users_rated"] > 0]

# Remove any rows with missing values.

games = games.dropna(axis=0)

# Make a histogram of all the ratings in the average_rating column.

plt.hist(games["average_rating"])

# Show the plot.

plt.show()

#correlation matrix

corrmat = games.corr()

fig = plt.figure(figsize = (12, 9))

sns.heatmap(corrmat, vmax=.8, square=True);

plt.show()

# Get all the columns from the dataframe.

columns = games.columns.tolist()

# Filter the columns to remove ones we don't want.

columns = [c for c in columns if c not in ["bayes_average_rating", "average_rating", "type", "name", "id"]]

# Store the variable we'll be predicting on.

target = "average_rating"

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

# Import a convenience function to split the sets.

from sklearn.cross_validation import train_test_split

# Generate the training set. Set random_state to be able to replicate results.

train = games.sample(frac=0.8, random_state=1)

# Select anything not in the training set and put it in the testing set.

test = games.loc[~games.index.isin(train.index)]

# Print the shapes of both sets.

print(train.shape)

print(test.shape)

# Import the linear regression model.

from sklearn.linear_model import LinearRegression

# Initialize the model class.

model = LinearRegression()

# Fit the model to the training data.

model.fit(train[columns], train[target])

# Import the scikit-learn function to compute error.

from sklearn.metrics import mean_squared_error

# Generate our predictions for the test set.

predictions = model.predict(test[columns])

# Compute error between our test predictions and the actual values.

mean_squared_error(predictions, test[target])

# Import the random forest model.

from sklearn.ensemble import RandomForestRegressor

# Initialize the model with some parameters.

model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10, random_state=1)

# Fit the model to the data.

model.fit(train[columns], train[target])

# Make predictions.

predictions = model.predict(test[columns])

# Compute the error.

mean_squared_error(predictions, test[target])

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

We have provided lines of code and images for example for you to understand you better.

We will use Pycharm IDE as it's easy and convenient to use.

Pycharm

We will start by importing important libraries

import pyttsx3 # pip install pyttsx3

import datetime # It's an prebuilt library

import speech_recognition as sr # pip install SpeechRecognition

import wikipedia

import smtplib

import webbrowser as wb

import psutil

import pyjokes

import os

import pyautogui

import json

from urllib.request import urlopen

import wolframalpha

import time

engine = pyttsx3.init()

wolframalpha_app_id = '99LHQ4-3LA69J466R'

def speak(audio):

engine.say(audio)

engine.runAndWait()

def time_():

Time = datetime.datetime.now().strftime("%I:%M:%S") # for 12 hours clock

speak("The current time is")

speak(Time)

def date_():

year = datetime.datetime.now().year

month = datetime.datetime.now().month

date = datetime.datetime.now().day

speak("The current date is")

speak(date)

speak(month)

speak(year)

def wishme():

speak("Welcome back Yash! Hope you have a good day")

time_()

date_()

# Greetings

hour = datetime.datetime.now().hour

if hour >= 6 and hour < 12:

speak("Good Morning Yash!")

elif hour >= 12 and hour < 18:

speak("Good Afternoon Yash!")

elif hour >= 18 and hour < 24:

speak("Good Evening Yash!")

else:

speak("Good Night Yash!")

speak("Jravis at your service. Please tell me Yash how can i help you today?")

def TakeCommand():

r = sr.Recognizer()

with sr.Microphone() as source:

print("Listening....")

r.pause_threshold = 1

audio = r.listen(source)

try:

print("Recognizing....")

query = r.recognize_google(audio, language='eng-in')

print(query)

except Exception as e:

print(e)

print("Say that again please....")

return "None"

return query

def sendEmail(to, content):

server = smtplib.SMTP('smtp.gmail.com', 587)

server.ehlo()

server.starttls()

server.login('yashv.088@gmail.com', 'YashMech2002')

server.send('yashv.088@gmail.com', to, content)

server.close()

def screenshot():

img = pyautogui.screenshot

img.save('C:/Users/91916/Pictures/Screenshots/screenshot.png')

def cpu():

usage = str(psutil.cpu_percent())

speak('Yash! CPU is at'+usage)

battery = psutil.sensors_battery()

speak('Yash! Battery is at')

speak(battery.percent)

def joke():

speak(pyjokes.get_joke())

if __name__ == "__main__":

wishme()

while True:

query = TakeCommand().lower()

if 'time' in query:

time_()

elif 'date' in query:

date_()

elif 'wikipedia' in query:

speak("Searching....")

query = query.replace('wikipedia', '')

result = wikipedia.summary(query, sentences=5)

speak('According to wikipedia..')

print(result)

speak(result)

elif 'send email' in query:

try:

speak("What should I say?")

content = TakeCommand()

speak("Who is the Reciever?")

reciever = input("Enter Reciever's email :")

sendEmail(to,content)

speak('Email has been saved')

except Exception as e:

print(e)

speak("Unable to send Email.")

elif 'search in chrome' in query:

speak('What should i search?')

chromepath = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'

search = TakeCommand().lower()

wb.get(chromepath).open_new_tab(search+'.com')

elif 'search youtube' in query:

speak('sure Yash!')

search_Term = TakeCommand().lower()

speak("Here we go to You Tube!")

wb.open('https://www.youtube.com/results?search_query='+search_Term)

elif 'search google' in query:

speak('What should i search?')

search_Term = TakeCommand().lower()

speak('Searching')

wb.open('https://www.google.com/search?q='+search_Term)

elif 'cpu' in query:

cpu()

elif 'joke' in query:

joke()

elif 'go offline' in query:

speak('as you said going offline Yash!')

quit()

elif 'word' in query:

speak('Opening MS word....')

ms_word = 'C:/Program Files/Microsoft Office/root/Office16/WINWORD.EXE'

os.startfile(ms_word)

elif 'excel' in query:

speak('Opening MS Excel...')

ms_exc = 'C:/Program Files/Microsoft Office/root/Office16/EXCEL.EXE'

os.startfile(ms_exc)

elif 'powerpoint' in query:

speak('Opening MS power point')

ms_pp = 'C:/Program Files/Microsoft Office/root/Office16/POWERPNT.EXE'

os.startfile(ms_pp)

elif 'wpotify' in query:

speak('Opening Spotify..')

spo = 'C:/Users/91916/AppData/Roaming/Spotify/Spotify'

os.startfile(spo)

elif 'whatsApp' in query:

speak('Opening WhatsApp')

wha = 'C:/Users/91916/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/WhatsApp/WhatsApp'

os.startfile(wha)

elif 'write a note' in query:

speak("What should i write Yash!..")

notes = TakeCommand()

file = open('notes.txt','w')

speak("Yash! Should i include date and time?")

ans = TakeCommand()

if 'yes' in ans or 'sure' in ans:

strTime = datetime.datetime.now().strftime("%H:%M:%S")

file.write(strTime)

file.write(':-')

file.write(notes)

speak("Done taking notes Yash!")

else:

file.write(notes)

elif 'show note' in query:

speak('showing notes..')

file = open("notes.txt",'r')

print(file.read())

speak(file.read())

elif 'screenshot' in query:

screenshot()

elif 'remember that' in query:

speak('What should i remember?')

memory = TakeCommand()

speak("You asked me to remember that"+memory)

remember = open('memory.txt','w')

remember.write(memory)

remember.close()

elif 'do you remember anything' in query:

remember = open('memory.txt','r')

speak('you asked me to remember that'+remember.read())

elif 'where is' in query:

query = query.replace("where is","")

location = query

speak("User said to locate"+location)

wb.open_new_tab("https://www.google.com/maps/place/"+location)

elif 'news' in query:

try:

jsonObj = urlopen("https://newsapi.org/v2/everything?domains=techcrunch.com,thenextweb.com&apiKey=fded868197784840843d5339f0241224")

data = json.load(jsonObj)

i = 1

speak("Here are some top Headlines from thr tech industry")

print("===========TOP HEADLINES============"+"\n")

for item in data['articles']:

print(str(i)+'. '+item['title']+'\n')

print(item['description']+'\n')

speak(item['title'])

i += 1

except Exception as e:

print(str(e))

elif 'calculate' in query:

client = wolframalpha.Client(wolframalpha_app_id)

indx = query.lower().split().index('calculate')

query = query.split()[indx + 1:]

res = client.query(''.join(query))

answer = next(res.results).text

print('The Answer is :'+answer)

speak('The Answer is '+answer)

elif 'what is' in query or 'who is' in query:

client = wolframalpha.Client(wolframalpha_app_id)

res = client.query()

try:

print(next(res.results).text)

speak(next(res.results).text)

except StopIteration:

print("No Results")

elif 'stop listening' in query:

speak("For how many seconds you want me to stop listening?")

ans = int(TakeCommand())

time.sleep(ans)

print(ans)

elif 'restart' in query:

os.system("shutdown /r /t 1")

elif 'shutdown' in query:

os.system("shutdown /s /t 1")

elif 'tell about me' in query:

speak("Your name is Yash you are studying mechanical engineering and you are interested in machine learning")

elif 'play music' in query:

songs_dir = 'Y:/Music'

music = os.listdir(songs_dir)

speak("Here's your fav song")

os.startfile(os.path.join(songs_dir, music[3]))

This is the code for our AI Assistant we have covered a lot of things in it we hope you all will make your own.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

2020-11-25T08:40:00+05:30

C

Number Guessing Game using C Language - C

Number Guessing Game We will make an interesting Game to understand C Languag…

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2020-11-20T16:48:00+05:30

machine learning

Board Game Predictions using ML - Machine Learning

Board Game Prediction using Machine Learning 1. Importing Libraries and Load…

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2020-11-20T16:42:00+05:30

coding python

Making own AI assistant using python - Python

Ai Assistant we are going to make our own virtual Ai Assistant using Python L…

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Popular Posts

Tags Clouds

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100" then you have to type any number between if you typed higher num then it will display lower num, pleaseand if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using Machine Learning

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python LanguageWe can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

We have provided lines of code and images for example for you to understand you better.

We will use Pycharm IDE as it's easy and convenient to use.

Pycharm

We will start by importing important libraries

This is the code for our AI Assistant we have covered a lot of things in it we hope you all will make your own.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100" then you have to type any number between if you typed higher num then it will display lower num, pleaseand if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using Machine Learning

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python LanguageWe can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

We have provided lines of code and images for example for you to understand you better.

We will use Pycharm IDE as it's easy and convenient to use.

Pycharm

We will start by importing important libraries

This is the code for our AI Assistant we have covered a lot of things in it we hope you all will make your own.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Number Guessing Game

We will make an interesting Game to understand C Language properly and to motivate us to code C language

In this Interesting Game, we have to Guess the Correct number

We have used loops in code

we will use clion to Code our Game

Clion

This is the code given below for the Number Guessing Game

After copying this code at Terminal which is down on left side you need to write gcc game.c

I have write game.c because I have saved file name as game.c you write whatever you like

The game will ask you first "Guess the number between 1 to 100" then you have to type any number between if you typed higher num then it will display lower num, pleaseand if you typed lower num then it will display higher num, please.

This is the code for our Number Guessing Game.

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Board Game Prediction using Machine Learning

2. Linear Regression

In the following cells, we will deploy a simple linear regression model to predict the average review of each board game. We will use the mean squared error as a performance metric. Furthermore, we will compare and contrast these results with the performance of an ensemble method.

This is the Prediction: 1.4458564605687358

If you have any doubt then you can ask in the comment section.

And Follow us by email and get notifications for free machine learning projects and codes.

Ai Assistant

we are going to make our own virtual Ai Assistant using Python LanguageWe can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

Board Game Prediction using
Machine Learning

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

Board Game Prediction using
Machine Learning

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.

The game will ask you first "Guess the number between 1 to 100"
then you have to type any number between
if you typed higher num then it will display lower num, please
and if you typed lower num then it will display higher num, please.

Board Game Prediction using
Machine Learning

we are going to make our own virtual Ai Assistant using Python Language
We can let that Assistant do lots of things for us like We can voice command, we can deliver greetings and we can tell the assistant to open applications for us.