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Commit2b28ff2

Browse files
authored
Merge pull request#120 from kashyap-singh/main
Rainwater Problem
2 parents446c6ab +674a2a5 commit2b28ff2

15 files changed

+1070
-1
lines changed

‎07 rainwater-trap.cpp‎

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#include<iostream>
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#include<vector>
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usingnamespacestd;
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inttrappedWater(vector<int> heights) {
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int n = heights.size();
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if(n<=2){
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return0;
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}
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vector<int>left(n,0),right(n,0);
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left[0] = heights[0];
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right[n-1] = heights[n-1];
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for(int i=1;i<n;i++){
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left[i] =max(left[i-1],heights[i]);
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right[n-i-1] =max(right[n-i],heights[n-i-1]);
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}
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int water =0;
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for(int i=0;i<n;i++){
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water +=min(left[i],right[i]) - heights[i];
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}
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return water;
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}
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intmain() {
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int n;
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cin>>n;
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vector<int> water;
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for(int i=0;i<n;i++)
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{
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int a;
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cin>>a;
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water.push_back(a);
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}
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cout<<trappedWater(water)<<endl;
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return0;
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}

‎ChatBot.py‎

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#%%
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importpyttsx3#pip install pyttsx3
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importspeech_recognitionassr#pip install speechRecognition
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importdatetime
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importwikipedia#pip install wikipedia
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importwebbrowser
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importos
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importsmtplib
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engine=pyttsx3.init('sapi5')
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voices=engine.getProperty('voices')
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# print(voices[1].id)
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engine.setProperty('voice',voices[0].id)
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defspeak(audio):
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engine.say(audio)
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engine.runAndWait()
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defwishMe():
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hour=int(datetime.datetime.now().hour)
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ifhour>=0andhour<12:
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speak("Good Morning!")
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elifhour>=12andhour<18:
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speak("Good Afternoon!")
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else:
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speak("Good Evening!")
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speak("I am Lucifer Sir. Please tell me how may I help you")
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deftakeCommand():
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#It takes microphone input from the user and returns string output
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r=sr.Recognizer()
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withsr.Microphone()assource:
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print("Listening...")
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r.pause_threshold=1
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audio=r.listen(source)
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try:
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print("Recognizing...")
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query=r.recognize_google(audio,language='en-in')
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print(f"User said:{query}\n")
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exceptExceptionase:
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# print(e)
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print("Say that again please...")
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return"None"
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returnquery
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defsendEmail(to,content):
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server=smtplib.SMTP('smtp.gmail.com',587)
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server.ehlo()
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server.starttls()
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server.login('youremail@gmail.com','your-password')
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server.sendmail('youremail@gmail.com',to,content)
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server.close()
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if__name__=="__main__":
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wishMe()
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whileTrue:
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# if 1:
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query=takeCommand().lower()
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# Logic for executing tasks based on query
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if'wikipedia'inquery:
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speak('Searching Wikipedia...')
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query=query.replace("wikipedia","")
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results=wikipedia.summary(query,sentences=2)
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speak("According to Wikipedia")
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print(results)
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speak(results)
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elif'open youtube'inquery:
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webbrowser.open("youtube.com")
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elif'open google'inquery:
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webbrowser.open("google.com")
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elif'open stackoverflow'inquery:
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webbrowser.open("stackoverflow.com")
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elif'play music'inquery:
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music_dir='D:\\Non Critical\\songs\\Favorite Songs2'
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songs=os.listdir(music_dir)
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print(songs)
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os.startfile(os.path.join(music_dir,songs[0]))
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elif'the time'inquery:
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strTime=datetime.datetime.now().strftime("%H:%M:%S")
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speak(f"Sir, the time is{strTime}")
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elif'open code'inquery:
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codePath="C:\\Users\\GAURAV\\AppData\\Local\\Programs\\Microsoft VS Code\\Code.exe"
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os.startfile(codePath)
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elif'email to harry'inquery:
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try:
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speak("What should I say?")
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content=takeCommand()
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to="harryyourEmail@gmail.com"
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sendEmail(to,content)
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speak("Email has been sent!")
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exceptExceptionase:
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print(e)
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speak("Sorry my friend gaurav. I am not able to send this email")

‎CreditRisk.py‎

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#%%
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importwarnings
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warnings.filterwarnings('ignore')
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importos
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importnumpyasnp
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importpandasaspd
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importscipy.statsasst
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importstatsmodels.apiassm
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importmatplotlib.pyplotasplt
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fromsklearnimportlinear_model
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fromsklearn.metricsimportmean_squared_error,r2_score,confusion_matrix,accuracy_score
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fromsklearn.model_selectionimporttrain_test_split
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fromsklearn.metricsimportroc_curve
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fromsklearn.metricsimportroc_auc_score
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# %%
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os.chdir('D:/machine_learning/DATA_SET/credit_risk')
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df_a=pd.read_csv('credit_risk.csv')
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df_a.head()
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# %%
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df_a.isnull().sum()
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# %%
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df_a.dropna(axis=0,inplace=True)
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df_a.isnull().sum()
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# %%
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df_a.dtypes
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# %%
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df_a["CoapplicantIncome"]=df_a["CoapplicantIncome"].astype('float')
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df_a["ApplicantIncome"]=df_a["ApplicantIncome"].astype('float')
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print("AFTER CHANGING INT & OBJECT TO FLOAT")
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df_a.dtypes
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# %%
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df_a["Dependents"].value_counts()
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# %%
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cleanup_nums= {
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"Married": {"Yes":1,"No":0},
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"Self_Employed": {"Yes":1,"No":0},
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"Property_Area": {"Rural":0,"Urban":1,"Semiurban":2},
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"Education": {"Graduate":1,"Not Graduate":0},
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"Loan_Status": {"Y":1,"N":0},
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"Gender": {"Male":1,"Female":0},
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"Dependents": {"0":0,"1":1,"2":2,"3+":3},
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}
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df_a.replace(cleanup_nums,inplace=True)
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df_a.head()
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# %%
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df_corr=df_a[['Gender','Married','Dependents','Education',
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'Self_Employed','ApplicantIncome','CoapplicantIncome','LoanAmount',
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'Loan_Amount_Term','Credit_History','Property_Area','Loan_Status']]
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df_corr.head()
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# %%
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plt.imshow(df_corr.corr(),cmap=plt.cm.Blues ,interpolation='nearest')
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cmap='coolwarm'
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plt.colorbar()
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tick_marks= [iforiinrange(len(df_corr.columns))]
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plt.xticks(tick_marks,df_corr.columns,rotation='vertical')
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plt.yticks(tick_marks,df_corr.columns)
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plt.show()
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#%%
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boolean_col='Loan_Status'
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cols= ['Gender','Married','Dependents','Education','Self_Employed',
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'ApplicantIncome','CoapplicantIncome','Credit_History'
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]
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xTrain=df_a[cols].values
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yTrain=df_a[boolean_col].values
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# %%
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st.chisqprob=lambdachisq,df:st.chi2.sf(chisq,df)
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model=sm.Logit(yTrain,xTrain )
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result=model.fit()
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result.summary(xname=cols,yname=boolean_col,title='Logit Model',alpha=1)
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# %%
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defcalculate_accuracy(predictions,real):
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correct=0
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foriinrange(len(predictions)):
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ifround(predictions[i])==round(real[i]):
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correct+=1
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returncorrect*1.0/len(predictions)
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# %%
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train_predictions=result.predict(xTrain)
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train_accuracy=calculate_accuracy(train_predictions,yTrain )
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print("Train Accuracy: ",train_accuracy*100 )
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# %%
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train_predictions= (train_predictions>0.5)
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train_cm=confusion_matrix(yTrain,train_predictions,labels= [1.0,0.0])
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print(train_cm )
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# %%
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labels= ['0','1']
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cm=train_cm
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fig=plt.figure()
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ax=fig.add_subplot(111)
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cax=ax.matshow(cm,cmap='viridis')
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plt.title('Confusion matrix')
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fig.colorbar(cax)
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ax.set_xticklabels(['']+labels)
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ax.set_yticklabels(['']+labels)
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r=0
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c=0
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forlistItemincm:
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forcellIteminlistItem:
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ax.text(c,r,cellItem,va='center',ha='center',color='r')
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c+=1
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c=0
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r+=1
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plt.xlabel('Actual label')
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plt.ylabel('Predicted label')
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plt.show()
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# %%

‎Detect Capital‎

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Detect Capital
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Level-Easy
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We define the usage of capitals in a word to be right when one of the following cases holds:
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All letters in this word are capitals, like "USA".
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All letters in this word are not capitals, like "leetcode".
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Only the first letter in this word is capital, like "Google".
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Given a string word, return true if the usage of capitals in it is right.
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Example 1:
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Input: word = "USA"
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Output: true
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Example 2:
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Input: word = "FlaG"
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Output: false
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Constraints:
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1 <= word.length <= 100
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word consists of lowercase and uppercase English letters.
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Time Complexity-o(n)
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Space Complexity-o(1)
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Java Solution
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class Solution {
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public boolean detectCapitalUse(String word) {
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if(word.length()>=2 && word.charAt(0)>=97 && word.charAt(0)<=123 && word.charAt(1)>=65 && word.charAt(1)<=91)
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return false;
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for(int i=1;i<word.length()-1;i++){
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if(word.charAt(i)>=65 && word.charAt(i)<=91){
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if(word.charAt(i+1)>=65 && word.charAt(i+1)<=91);
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else
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return false;
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}
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else{
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if(word.charAt(i+1)>=97 && word.charAt(i+1)<=123);
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else
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return false;
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}
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}
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return true;
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}
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}

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