Oct 2, 2022 · Oct 2, 2022 · Oct 2, 2022 · Oct 2, 2022 · Oct 2, 2022 · Oct 2, 2022
diff --git a/07 rainwater-trap.cpp b/07 rainwater-trap.cpp
 #include <iostream>
 #include <vector>
 using namespace std;


 int trappedWater(vector<int> heights) {
 int n = heights.size();
 if(n<=2){
 return 0;
 }
 vector<int> left(n,0), right(n,0);
 left[0] = heights[0];
 right[n-1] = heights[n-1];

 for(int i=1;i<n;i++){
 left[i] = max(left[i-1],heights[i]);
 right[n-i-1] = max(right[n-i],heights[n-i-1]);
 }
 int water = 0;
 for(int i=0;i<n;i++){
 water += min(left[i],right[i]) - heights[i];
 }
  return water;
 }

 int main() {
 int n;
 cin>>n;
 vector<int> water;
 for(int i=0;i<n;i++)
 {
 int a;
 cin>>a;
 water.push_back(a);
 }
    cout<<trappedWater(water)<<endl;
    return 0;
 }
diff --git a/ChatBot.py b/ChatBot.py
 #%%
 import pyttsx3 #pip install pyttsx3
 import speech_recognition as sr #pip install speechRecognition
 import datetime
 import wikipedia #pip install wikipedia
 import webbrowser
 import os
 import smtplib

 engine = pyttsx3.init('sapi5')
 voices = engine.getProperty('voices')
 # print(voices[1].id)
 engine.setProperty('voice', voices[0].id)


 def speak(audio):
    engine.say(audio)
    engine.runAndWait()


 def wishMe():
    hour = int(datetime.datetime.now().hour)
    if hour>=0 and hour<12:
        speak("Good Morning!")

    elif hour>=12 and hour<18:
        speak("Good Afternoon!")

    else:
        speak("Good Evening!")

    speak("I am Lucifer Sir. Please tell me how may I help you")

 def takeCommand():
    #It takes microphone input from the user and returns string output

    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='en-in')
        print(f"User said: {query}\n")

    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('youremail@gmail.com', 'your-password')
    server.sendmail('youremail@gmail.com', to, content)
    server.close()

 if __name__ == "__main__":
    wishMe()
    while True:
    # if 1:
        query = takeCommand().lower()

        # Logic for executing tasks based on query
        if 'wikipedia' in query:
            speak('Searching Wikipedia...')
            query = query.replace("wikipedia", "")
            results = wikipedia.summary(query, sentences=2)
            speak("According to Wikipedia")
            print(results)
            speak(results)

        elif 'open youtube' in query:
            webbrowser.open("youtube.com")

        elif 'open google' in query:
            webbrowser.open("google.com")

        elif 'open stackoverflow' in query:
            webbrowser.open("stackoverflow.com")


        elif 'play music' in query:
            music_dir = 'D:\\Non Critical\\songs\\Favorite Songs2'
            songs = os.listdir(music_dir)
            print(songs)
            os.startfile(os.path.join(music_dir, songs[0]))

        elif 'the time' in query:
            strTime = datetime.datetime.now().strftime("%H:%M:%S")
            speak(f"Sir, the time is {strTime}")

        elif 'open code' in query:
            codePath = "C:\\Users\\GAURAV\\AppData\\Local\\Programs\\Microsoft VS Code\\Code.exe"
            os.startfile(codePath)

        elif 'email to harry' in query:
            try:
                speak("What should I say?")
                content = takeCommand()
                to = "harryyourEmail@gmail.com"
                sendEmail(to, content)
                speak("Email has been sent!")
            except Exception as e:
                print(e)
                speak("Sorry my friend gaurav. I am not able to send this email")
diff --git a/CreditRisk.py b/CreditRisk.py
 #%%
 import warnings
 warnings.filterwarnings('ignore')

 import os
 import numpy as np
 import pandas as pd
 import scipy.stats as st
 import statsmodels.api as sm
 import matplotlib.pyplot as plt

 from sklearn import linear_model
 from sklearn.metrics import mean_squared_error, r2_score, confusion_matrix, accuracy_score
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import roc_curve
 from sklearn.metrics import roc_auc_score
 # %%
 os.chdir('D:/machine_learning/DATA_SET/credit_risk')
 df_a = pd.read_csv('credit_risk.csv')
 df_a.head()
 # %%
 df_a.isnull().sum()
 # %%
 df_a.dropna(axis=0,inplace=True)
 df_a.isnull().sum()
 # %%
 df_a.dtypes
 # %%
 df_a["CoapplicantIncome"] = df_a["CoapplicantIncome"].astype('float')
 df_a["ApplicantIncome"] = df_a["ApplicantIncome"].astype('float')
 print("AFTER CHANGING INT & OBJECT TO FLOAT")
 df_a.dtypes
 # %%
 df_a["Dependents"].value_counts()
 # %%
 cleanup_nums = {
    "Married": {"Yes": 1, "No": 0},
    "Self_Employed": {"Yes": 1, "No": 0},
    "Property_Area": {"Rural":0, "Urban":1, "Semiurban":2},
    "Education": {"Graduate": 1, "Not Graduate": 0},
    "Loan_Status": { "Y": 1, "N": 0},
    "Gender": { "Male": 1, "Female": 0},
    "Dependents": { "0": 0, "1": 1, "2": 2, "3+": 3},
 }

 df_a.replace(cleanup_nums, inplace=True)
 df_a.head()
 # %%
 df_corr = df_a[[ 'Gender', 'Married', 'Dependents', 'Education',
       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',
       'Loan_Amount_Term', 'Credit_History', 'Property_Area','Loan_Status']]

 df_corr.head()
 # %%
 plt.imshow(df_corr.corr(), cmap=plt.cm.Blues , interpolation='nearest')
 cmap='coolwarm'
 plt.colorbar()
 tick_marks = [i for i in range(len(df_corr.columns))]
 plt.xticks(tick_marks, df_corr.columns, rotation='vertical')
 plt.yticks(tick_marks, df_corr.columns)
 plt.show()
 #%%
 boolean_col = 'Loan_Status'
 cols = ['Gender','Married','Dependents','Education','Self_Employed',
        'ApplicantIncome', 'CoapplicantIncome', 'Credit_History'
        ]

 xTrain = df_a[cols].values
 yTrain = df_a[boolean_col].values
 # %%
 st.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)

 model = sm.Logit( yTrain, xTrain )
 result = model.fit()
 result.summary( xname=cols, yname=boolean_col,title='Logit Model', alpha=1)
 # %%
 def calculate_accuracy( predictions, real):
    correct = 0
    for i in range(len(predictions)):
        if round(predictions[i]) == round(real[i]):
            correct += 1
    return correct * 1.0 / len(predictions)
 # %%
 train_predictions = result.predict(xTrain)

 train_accuracy = calculate_accuracy( train_predictions, yTrain )
 print("Train Accuracy: ", train_accuracy * 100 )
 # %%
 train_predictions = (train_predictions > 0.5)

 train_cm = confusion_matrix( yTrain, train_predictions, labels = [1.0, 0.0])
 print(train_cm )
 # %%
 labels = ['0', '1']
 cm = train_cm
 fig = plt.figure()
 ax = fig.add_subplot(111)
 cax = ax.matshow(cm, cmap='viridis')
 plt.title('Confusion matrix')
 fig.colorbar(cax)
 ax.set_xticklabels([''] + labels)
 ax.set_yticklabels([''] + labels)

 r=0
 c=0
 for listItem in cm:
    for cellItem in listItem:
        ax.text(c,r, cellItem, va='center', ha='center', color='r')
        c+=1
    c=0
    r+=1

 plt.xlabel('Actual label')
 plt.ylabel('Predicted label')
 plt.show()
 # %%
diff --git a/Detect Capital b/Detect Capital
 Detect Capital
 Level-Easy

 We define the usage of capitals in a word to be right when one of the following cases holds:
 All letters in this word are capitals, like "USA".
 All letters in this word are not capitals, like "leetcode".
 Only the first letter in this word is capital, like "Google".
 Given a string word, return true if the usage of capitals in it is right.
 Example 1:
 Input: word = "USA"
 Output: true
 Example 2:
 Input: word = "FlaG"
 Output: false
 Constraints:
 1 <= word.length <= 100
 word consists of lowercase and uppercase English letters.

 Time Complexity-o(n)
 Space Complexity-o(1)

 Java Solution

 class Solution {
    public boolean detectCapitalUse(String word) {
        if(word.length()>=2 && word.charAt(0)>=97 && word.charAt(0)<=123   && word.charAt(1)>=65 && word.charAt(1)<=91)
            return false;
        for(int i=1;i<word.length()-1;i++){
            if(word.charAt(i)>=65 && word.charAt(i)<=91){
                if(word.charAt(i+1)>=65 && word.charAt(i+1)<=91);
                else
                    return false;
            }
            else{
 if(word.charAt(i+1)>=97 && word.charAt(i+1)<=123);
                else
                    return false;
            }
        }
        return true;
    }
 }
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,38 @@
		#include <iostream>
		#include <vector>
		using namespace std;


		int trappedWater(vector<int> heights) {
		int n = heights.size();
		if(n<=2){
		return 0;
		}
		vector<int> left(n,0), right(n,0);
		left[0] = heights[0];
		right[n-1] = heights[n-1];

		for(int i=1;i<n;i++){
		left[i] = max(left[i-1],heights[i]);
		right[n-i-1] = max(right[n-i],heights[n-i-1]);
		}
		int water = 0;
		for(int i=0;i<n;i++){
		water += min(left[i],right[i]) - heights[i];
		}
		return water;
		}

		int main() {
		int n;
		cin>>n;
		vector<int> water;
		for(int i=0;i<n;i++)
		{
		int a;
		cin>>a;
		water.push_back(a);
		}
		cout<<trappedWater(water)<<endl;
		return 0;
		}
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,110 @@
		#%%
		import pyttsx3 #pip install pyttsx3
		import speech_recognition as sr #pip install speechRecognition
		import datetime
		import wikipedia #pip install wikipedia
		import webbrowser
		import os
		import smtplib

		engine = pyttsx3.init('sapi5')
		voices = engine.getProperty('voices')
		# print(voices[1].id)
		engine.setProperty('voice', voices[0].id)


		def speak(audio):
		engine.say(audio)
		engine.runAndWait()


		def wishMe():
		hour = int(datetime.datetime.now().hour)
		if hour>=0 and hour<12:
		speak("Good Morning!")

		elif hour>=12 and hour<18:
		speak("Good Afternoon!")

		else:
		speak("Good Evening!")

		speak("I am Lucifer Sir. Please tell me how may I help you")

		def takeCommand():
		#It takes microphone input from the user and returns string output

		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='en-in')
		print(f"User said: {query}\n")

		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('youremail@gmail.com', 'your-password')
		server.sendmail('youremail@gmail.com', to, content)
		server.close()

		if __name__ == "__main__":
		wishMe()
		while True:
		# if 1:
		query = takeCommand().lower()

		# Logic for executing tasks based on query
		if 'wikipedia' in query:
		speak('Searching Wikipedia...')
		query = query.replace("wikipedia", "")
		results = wikipedia.summary(query, sentences=2)
		speak("According to Wikipedia")
		print(results)
		speak(results)

		elif 'open youtube' in query:
		webbrowser.open("youtube.com")

		elif 'open google' in query:
		webbrowser.open("google.com")

		elif 'open stackoverflow' in query:
		webbrowser.open("stackoverflow.com")


		elif 'play music' in query:
		music_dir = 'D:\\Non Critical\\songs\\Favorite Songs2'
		songs = os.listdir(music_dir)
		print(songs)
		os.startfile(os.path.join(music_dir, songs[0]))

		elif 'the time' in query:
		strTime = datetime.datetime.now().strftime("%H:%M:%S")
		speak(f"Sir, the time is {strTime}")

		elif 'open code' in query:
		codePath = "C:\\Users\\GAURAV\\AppData\\Local\\Programs\\Microsoft VS Code\\Code.exe"
		os.startfile(codePath)

		elif 'email to harry' in query:
		try:
		speak("What should I say?")
		content = takeCommand()
		to = "harryyourEmail@gmail.com"
		sendEmail(to, content)
		speak("Email has been sent!")
		except Exception as e:
		print(e)
		speak("Sorry my friend gaurav. I am not able to send this email")
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,116 @@
		#%%
		import warnings
		warnings.filterwarnings('ignore')

		import os
		import numpy as np
		import pandas as pd
		import scipy.stats as st
		import statsmodels.api as sm
		import matplotlib.pyplot as plt

		from sklearn import linear_model
		from sklearn.metrics import mean_squared_error, r2_score, confusion_matrix, accuracy_score
		from sklearn.model_selection import train_test_split
		from sklearn.metrics import roc_curve
		from sklearn.metrics import roc_auc_score
		# %%
		os.chdir('D:/machine_learning/DATA_SET/credit_risk')
		df_a = pd.read_csv('credit_risk.csv')
		df_a.head()
		# %%
		df_a.isnull().sum()
		# %%
		df_a.dropna(axis=0,inplace=True)
		df_a.isnull().sum()
		# %%
		df_a.dtypes
		# %%
		df_a["CoapplicantIncome"] = df_a["CoapplicantIncome"].astype('float')
		df_a["ApplicantIncome"] = df_a["ApplicantIncome"].astype('float')
		print("AFTER CHANGING INT & OBJECT TO FLOAT")
		df_a.dtypes
		# %%
		df_a["Dependents"].value_counts()
		# %%
		cleanup_nums = {
		"Married": {"Yes": 1, "No": 0},
		"Self_Employed": {"Yes": 1, "No": 0},
		"Property_Area": {"Rural":0, "Urban":1, "Semiurban":2},
		"Education": {"Graduate": 1, "Not Graduate": 0},
		"Loan_Status": { "Y": 1, "N": 0},
		"Gender": { "Male": 1, "Female": 0},
		"Dependents": { "0": 0, "1": 1, "2": 2, "3+": 3},
		}

		df_a.replace(cleanup_nums, inplace=True)
		df_a.head()
		# %%
		df_corr = df_a[[ 'Gender', 'Married', 'Dependents', 'Education',
		'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',
		'Loan_Amount_Term', 'Credit_History', 'Property_Area','Loan_Status']]

		df_corr.head()
		# %%
		plt.imshow(df_corr.corr(), cmap=plt.cm.Blues , interpolation='nearest')
		cmap='coolwarm'
		plt.colorbar()
		tick_marks = [i for i in range(len(df_corr.columns))]
		plt.xticks(tick_marks, df_corr.columns, rotation='vertical')
		plt.yticks(tick_marks, df_corr.columns)
		plt.show()
		#%%
		boolean_col = 'Loan_Status'
		cols = ['Gender','Married','Dependents','Education','Self_Employed',
		'ApplicantIncome', 'CoapplicantIncome', 'Credit_History'
		]

		xTrain = df_a[cols].values
		yTrain = df_a[boolean_col].values
		# %%
		st.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)

		model = sm.Logit( yTrain, xTrain )
		result = model.fit()
		result.summary( xname=cols, yname=boolean_col,title='Logit Model', alpha=1)
		# %%
		def calculate_accuracy( predictions, real):
		correct = 0
		for i in range(len(predictions)):
		if round(predictions[i]) == round(real[i]):
		correct += 1
		return correct * 1.0 / len(predictions)
		# %%
		train_predictions = result.predict(xTrain)

		train_accuracy = calculate_accuracy( train_predictions, yTrain )
		print("Train Accuracy: ", train_accuracy * 100 )
		# %%
		train_predictions = (train_predictions > 0.5)

		train_cm = confusion_matrix( yTrain, train_predictions, labels = [1.0, 0.0])
		print(train_cm )
		# %%
		labels = ['0', '1']
		cm = train_cm
		fig = plt.figure()
		ax = fig.add_subplot(111)
		cax = ax.matshow(cm, cmap='viridis')
		plt.title('Confusion matrix')
		fig.colorbar(cax)
		ax.set_xticklabels([''] + labels)
		ax.set_yticklabels([''] + labels)

		r=0
		c=0
		for listItem in cm:
		for cellItem in listItem:
		ax.text(c,r, cellItem, va='center', ha='center', color='r')
		c+=1
		c=0
		r+=1

		plt.xlabel('Actual label')
		plt.ylabel('Predicted label')
		plt.show()
		# %%
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,42 @@
		Detect Capital
		Level-Easy

		We define the usage of capitals in a word to be right when one of the following cases holds:
		All letters in this word are capitals, like "USA".
		All letters in this word are not capitals, like "leetcode".
		Only the first letter in this word is capital, like "Google".
		Given a string word, return true if the usage of capitals in it is right.
		Example 1:
		Input: word = "USA"
		Output: true
		Example 2:
		Input: word = "FlaG"
		Output: false
		Constraints:
		1 <= word.length <= 100
		word consists of lowercase and uppercase English letters.

		Time Complexity-o(n)
		Space Complexity-o(1)

		Java Solution

		class Solution {
		public boolean detectCapitalUse(String word) {
		if(word.length()>=2 && word.charAt(0)>=97 && word.charAt(0)<=123 && word.charAt(1)>=65 && word.charAt(1)<=91)
		return false;
		for(int i=1;i<word.length()-1;i++){
		if(word.charAt(i)>=65 && word.charAt(i)<=91){
		if(word.charAt(i+1)>=65 && word.charAt(i+1)<=91);
		else
		return false;
		}
		else{
		if(word.charAt(i+1)>=97 && word.charAt(i+1)<=123);
		else
		return false;
		}
		}
		return true;
		}
		}