initial commit

app.py

@@ -0,0 +1,262 @@
+# importing libraries
+import openai
+import re
+import gradio as gr
+from pydub import AudioSegment
+import components
+import dbQuery
+
+# getting openAI key
+#openai.api_key = open("requirements/api_key.txt", "r").read().strip()
+openai.api_key = "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"
+# defining the folder for interview types. Change here.
+interview_directory = "data_scientist"
+
+#defining the file_paths
+rubric_filepath = interview_directory + "/evaluation_rubric.txt"
+system_message_filepath = interview_directory + "/system_message.txt"
+assistant_first_message_filepath = interview_directory + "/assistant_first_message.txt"
+
+max_rows = 30 # max number of rows to show in feedback table
+max_conversations = 25 # max conversations allowed 
+
+#reading requried files
+with open(system_message_filepath) as f:
+	system_message = ' '.join(f.readlines())
+with open(assistant_first_message_filepath) as f:
+	assistant_first_message = ' '.join(f.readlines())
+
+sessionID=0
+def get_new_sessionID():
+	print("new session id: ")
+	sessionID = dbQuery.DB_SessionID()
+	print(sessionID)
+	return sessionID
+# if sessionID==0:
+# 	gr.reload
+############################
+###    DB Connection     ###
+############################
+
+
+#defining functions for call
+#Todo: move to separate file and then call.
+
+def getchat(message_history=None):
+	"""
+	gets a chat response from the API given a user input and message history
+	"""
+	connection_error = False
+	try:
+		reply_content = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=message_history).choices[0].message.content 
+	except openai.error.APIConnectionError as e:
+		connection_error = e
+		reply_content = "I'm sorry, I'm having trouble connecting to the server. Please try again later."
+
+	reply_content = reply_content.replace("\n\n", "\n")
+	return reply_content, connection_error
+
+def grade_response(question, user_input):
+	"""
+	Takes an interview question and a user response. Evaluates the response using an evaluation rubric and 
+	returns a 1-10 grade and feedback as a dict e.g. {grade: 10, feedback: "Great answer!"}
+	"""
+
+	# Read the rubrics used by grade_response()
+	with open(rubric_filepath) as f:
+		eval_rubric = ' '.join(f.readlines())
+
+	prompt = eval_rubric + "\n" + "Question: {0}".format(question) + "\n" + "User response: {0}".format(user_input)
+	model = "text-davinci-002"
+
+	# get the grade and feedback from ChatGPT API
+	connection_error = False
+	try:
+		chat_response = openai.Completion.create(engine=model, 
+					   prompt=prompt,
+					   max_tokens=200,
+					   temperature=0.5,
+					   n=1,
+					   stop=None,
+					   frequency_penalty=0,
+					   presence_penalty=0)
+	except openai.error.APIConnectionError as e:
+		connection_error = e
+		message = "I'm sorry, I'm having trouble connecting to the server. Please try again later."
+
+	message = chat_response.choices[0].text.strip()
+	
+	# convert to lowercase
+	message = message.lower()
+	# remove single quotes from the string
+	message = message.replace("'", "")
+	# remove double quotes from the string
+	message = message.replace('"', '')
+
+	# use regex to get the key and value
+	try:
+		grade = re.findall(r'(?<=grade: )\d+', message)[0]
+	except IndexError:
+		grade = None
+	
+	try:
+		feedback = re.findall(r'(?<=feedback: ).+', message)[0]
+		feedback = feedback.replace('"', '')
+		feedback = feedback.replace("}", "")
+		feedback = feedback.replace("{", "")
+		feedback = feedback.replace('\n\n', '\n')	
+	except IndexError:
+		feedback = None
+		feedback = "No feedback provided for this response."
+
+	# write grade and feedback to a text file
+	with open('scores.txt', 'a') as f:
+		f.write(message)
+		f.write("\n")
+		f.write("grade={0}".format(grade))
+		f.write("\n")
+		f.write("feedback={0}".format(feedback))
+		f.write("\n\n")
+		
+	message = {"grade": grade, "feedback": feedback}
+	return message, connection_error
+
+def show_feedback_fn(scores):
+		if len(scores) > max_rows:
+			scores = scores[0:(max_rows+1)]
+		else:
+			scores = scores[0:]
+
+		for i, score in enumerate(scores): 
+			if i == 0:
+				score["question"]=score["question"].split("Let's start with the first question:")[1]
+			feedback_array[i*4] = gr.update(value=score["question"], visible=True)
+			feedback_array[i*4+1] = gr.update(value=score["response"], visible=True)
+			feedback_array[i*4+2] = gr.update(value=score["score"], visible=True)
+			feedback_array[i*4+3] = gr.update(value=score["feedback"], visible=True)
+
+		try:
+			score_number = sum([int(score["score"]) for score in scores if score["score"] is not None]) / (10*len([score["score"] for score in scores if score["score"] is not None]))
+			score_number = round(score_number*100, 1)
+		except ZeroDivisionError:
+			score_number = 0
+		
+		score_number = str(score_number) + "%"
+		feedback_array[-1] = gr.update(value=score_number, visible=True) # the last element of feedback array is the score number
+
+		return feedback_array
+
+
+def user(user_message, gr_chat_history, message_history, scores):
+	return "", gr_chat_history + [[user_message, None]], message_history, scores, None
+
+
+def bot(gr_chat_history, message_history, scores):
+		last_user_message = gr_chat_history[-1][0] # get the last user message
+		last_question = message_history[-1]["content"] # question asked by the assistant (for grading)
+		message_history.append({"role": "user", "content": last_user_message})
+		# grade the user's response
+		score_and_feedback, connection_error_grade = grade_response(question=last_question, user_input=last_user_message)
+		if connection_error_grade:
+			raise gr.Error("API connection error! Refresh page to restart.")
+		
+		scores.append({"question": last_question, "response": last_user_message, "score": score_and_feedback["grade"], "feedback": score_and_feedback["feedback"]})	
+		
+        # get chat response from ChatCompletion API
+		reply_content, connection_error_chat = getchat(message_history=message_history)
+		if connection_error_chat:
+			raise gr.Error("API connection error! Refresh page to restart.")
+		message_history.append({"role": "assistant", "content": reply_content})
+		gr_chat_history[-1][1] = reply_content.replace(">", "")
+		result = dbQuery.insertData(sessionID,scores,len(gr_chat_history))
+		print("score XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
+		print(scores)
+		print("score XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
+		if len(gr_chat_history) > max_conversations:
+			gr_chat_history[-1][1] = '''You have reached the end of the interview. Please click "End Interview and See Score & Feedback"'''
+			return gr_chat_history, message_history, scores
+		else:
+			return gr_chat_history, message_history, scores
+	
+
+def convert_audio_file(input_file_path, output_file_path, output_format):
+    audio = AudioSegment.from_file(input_file_path)
+    audio.export(output_file_path, format=output_format)
+    return output_file_path
+    # audio.upload(upload_to_s3)
+	# return out_path
+
+def whisper_transcribe(input_filepath):
+    output_filepath = "./"+str(sessionID)+".mp3"
+    output_filepath = convert_audio_file(input_filepath, output_filepath, "mp3")
+    audio_file= open(output_filepath, "rb")
+    transcript = openai.Audio.transcribe("whisper-1", audio_file, target_language="en")
+    return transcript["text"]
+
+with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"]),onrefresh=get_new_sessionID) as demo:
+	session_status = 0
+	message_history = gr.State([
+		{"role": "system", "content": system_message}, 
+		{"role": "assistant", "content": assistant_first_message}]) # message_history is used by the GPT ChatCompletion() method
+	scores = gr.State([]) # store question, response, grades, feedback in a list of dicts [{question}, {response}, {grade}, {feedback}]
+	# gr.State.add_func('DB_SessionID',dbQuery.DB_SessionID)
+	# my_fun=gr.State.DB_SessionID
+	# sessionID = my_fun()
+
+	#checking session existence
+	#session__Id=gr.state(value=0)
+
+	
+
+
+	gr.Markdown(components.heading_one) # show the heading and instructions
+	line_below_heading = gr.Markdown("____")
+	gr.Markdown(assistant_first_message)
+	chatbot = gr.Chatbot(lines=4, label="Interview Assistant") # list-like object [[user_response, bot_response]]
+	session_status=0
+	if(session_status==0):
+		sessionID = dbQuery.DB_SessionID()
+		print("return from db call", sessionID)
+		session_status=1
+	with gr.Row():
+		audio_input = gr.Audio(source="microphone", type="filepath")
+		transcribe_button = gr.Button("Transcribe")
+	msg = gr.Textbox(lines=4, label="Your response", placeholder="Record from mic, transcribe and press Shift+Enter to submit.")
+	
+	some_line = gr.Markdown("##")
+	horizontal_line_one = gr.Markdown("____")
+	show_feedback = gr.Button("End Interview and See Score & Feedback")
+	horizontal_line_two = gr.Markdown("____")
+
+	another_line = gr.Markdown("##")
+	
+	feedback_array = []
+	score_number = gr.Textbox(label="% Score", visible=False)
+	
+	for n in range(max_rows):
+		with gr.Row() as output_row:
+			question_column = gr.Textbox(label="Question", visible=False)
+			response_column = gr.Textbox(label="Your response", visible=False)
+			score_column = gr.Textbox(label="Score", visible=False)
+			feedback_column = gr.Textbox(label="Feedback", visible=False)
+			feedback_array.append(question_column)
+			feedback_array.append(response_column)
+			feedback_array.append(score_column)
+			feedback_array.append(feedback_column)
+	
+
+	feedback_array.append(score_number) # the last element of feedback array is the score number
+	msg.submit(user, 
+        [msg, chatbot, message_history, scores], 
+        [msg, chatbot, message_history, scores, audio_input], 
+        queue=False).then(bot, 
+            [chatbot, message_history, scores], 
+            [chatbot, message_history, scores])
+	transcribe_button.click(whisper_transcribe, audio_input, msg)
+	show_feedback.click(show_feedback_fn,
+        inputs=scores,
+        outputs=feedback_array)
+            
+# run only if directly executed on terminal
+if __name__ == "__main__":
+	demo.launch(share=True)

components.py

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+# Heading of the webpage
+# Todo: style the element
+heading_one = '''# <span style="color:gray">GPT Interview Assistant by </span><span style="color:firebrick">upGrad</span>'''

data_scientist/assistant_first_message.txt

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+> Hello and welcome to the **mock interview on statistics and machine learning!** Please answer the questions as if you are in a real job interview. I will evaluate your response and ask follow-up questions. I will not reveal the right answers to you, but I will guide you in the right direction if you make a mistake. 
+>
+> **Instructions:**
+> - To record your answer, click "Record from microphone" and hit 'Transcribe". Press `Shift+Enter` to submit your answer.
+> - You can end the interview at any time by clicking the "End Interview and Show Score & Feedback" button. You will be shown your final score and feedback.
+> - To restart a new interview, refresh the page.
+
+> **Let's start with the first question: Can you briefly describe the difference between supervised and unsupervised learning techniques? Provide one example of each.**

data_scientist/evaluation_rubric.txt

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+Context: You are in a job interview that is being conducted for the role of a data scientist, machine learning engineer, data analyst, business analyst etc. The candidate is asked a question by the interviewer and the candidate shares their response. 
+
+Role: You are a data science and machine learning expert conducting job interviews of candidates for the roles of data scientist, machine learning engineer, data analyst, business analyst etc. Your job is to understand the interview question asked, evaluate the candidate's response, and provide a grade (score) on a scale of 1-10 (10 being the best possible response for the given question). You *never* reveal the right answer to the user, you only ask follow-up questions wherever the user makes an error. Else, you ask further questions. Use the evaluation guidelines provided below to evaluate the response. 
+
+Provide your response in the format of a Python dictionary {grade: grade, feedback: feedback}.
+
+
+Evaluation guidelines below:
+- Evaluate each response against three main criteria: 1) technical correctness, 2) exhaustiveness (completeness of information), i.e. detailed descriptions of the relevant theory/principles., 3) clarity of explanations (i.e use of examples, analogies, etc) 
+- Award grades (scores) of 9 or 10 only if the response is technically correct, exhaustive with complete information of the relevant theory/principles and clearly explained with examples, analogies
+- Award grade (scores) of 7 or 8 if the response is technically correct but lacks either exhaustiveness or clarity of explanation
+- Award grade (scores) less than 7 if any part of the response is technically correct. If more than one elements are incorrect, award less than 5 grade (score).
+- For each response, provide detailed feedback which mentions specific recommendations to improve the response. 
+- Some ideal samples of user response, grade and feedback are provided below:
+
+Example-1: 
+{Interview question: Explain the concept of multicollinearity and how it is used in linear regression,
+Candidate response: Multicollinearity is a phenomena where some of the features are correlated with each other. High multicollinearity is not desirable since it may cause the model to overfit the training data,
+Grade: 6,
+Feedback: While your response is partially correct in that multicollinearity refers to correlation among features and that it may cause overfitting, it missed mentioning another problem - it makes it hard to interpret the model coefficients. Also, you could mention that is is measured using VIF (variance inflation factor), a statistical measure that measures the extent of correlation among features, and briefly explain how VIF works. Also, the response can be improved by providing a real example, such as body weight and height are likely to be correlated, or total sales and price per unit are likely to be correlated
+}
+
+
+Example-2: 
+{Interview question: What is your approach to handle missing values in a dataset?
+
+Candidate response: It depends on the situation and the type of data we are dealing with. One of the ways of handling missing data is by dropping all those observations where data is missing. For this, we need to be sure that the data is missing due to chance alone. 
+Some of the other ways in which missing values can be handled are:
+
+We can replace or impute the missing values with the mean or median for numeric data and mode for categorical data. Essentially, we replace the missing values with measures of the central tendency for each column in the dataset.  
+Another way in which we can handle missing values in categorical data is by creating another level i.e. another unique value which replaces all the missing values in that column. 
+
+We can also run predictive models which can impute missing data. Here, we treat the column with missing values as the target column. The training data will be the rows in which the target column has values and the test data will comprise of the rows where the target column does not have values. We can now build a model using the training data and use this model to predict the missing values.
+These are some of the ways in which missing values are handled in the dataset,
+Grade: 9-10,
+Feedback:  Your answer is exhaustive and categorically explains how to handle missing data. You can maybe add an example of how you have handled some special cases of missing data in any of the past data projects that you have done.
+}
+
+Example-3: 
+{Interview question: Why is accuracy not a good measure for classification problems?
+Candidate response: Accuracy is not a good measure for classification problems because it gives equal importance to both false positives and false negatives. However, this may not be the case in most business problems. For example, in the case of cancer prediction, declaring cancer as benign is more serious than wrongly informing the patient that he is suffering from cancer. Accuracy gives equal importance to both cases and cannot differentiate between them,
+Grade: 7-8,
+Feedback: Your answer is factually correct and you explained it with an example explaining accuracy's shortcomings as a measure. To make your answer better, you should also give another example where accuracy is not a good measure for a problem, and to give a better impression, you can mention other metrics that can be used instead of accuracy, such as, 'precision' and 'recall'. 
+}
+
+Example-4: 
+{Interview question: Explain the concept of joins in SQL.
+Candidate response: Joins are used to merge two datasets in SQL to create a single master dataset.
+Grade: 4
+Feedback: Your answer just touches the surface and explains what a join does, which is a very fundamental and basic concept. You need to go beyond the definition and explain the need for joins and the different ways in which you can join two datasets in SQL. You should ideally explain left join, right join, inner join and outer join each with an example to exhaustively answer the question at hand. For example, when explaining outer joins, you can mention how it works, and why it is necessary in the cases where we need to retain all the values from both tables, even though there might be missing data.
+}
+
+Example-4: 
+{Interview question: Explain the difference between precision and recall.
+Candidate response: Precision is a metric used to evaluate regression models or numeric output while recall is a metric used to evaluate classification models. 
+Grade: 0
+Feedback: Your answer in incorrect. Precision and recall are both evaluation metrics for classification models. Precision measures the number of correct positive predictions out of all positive predictions while recall measures the number of correct positive predictions out of all actual positive values. Ideally, you should provide an example of when you would use precision over recall and vice versa.
+}

data_scientist/system_message.txt

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+You are a Data Science expert conducting technical interviews of candidates for roles such as Data Analyst, Business Analyst, Data Scientist, ML Engineer, etc. As the interviewer, you ask interview questions to the candidate on topics of statistics and machine learning, evaluate the candidate's response, and ask a follow-up question. You *never* reveal the right answer to the user, you only ask follow-up questions wherever the user makes an error. Else, you ask further questions. Ask specific questions that test deep knowledge and understanding of statistics and machine learning. Ask questions in a logical flow, as if in a real job interview. Some representative interview questions are given below. You may use these but also create similar variants of questions at varying level of difficulty.
+
+Sample representative questions:
+1. How do you handle missing values in a dataset when starting a data project?
+2. What are the different ways in which outliers can be handled?
+3. Does a correlation coefficient of 0 between two numeric variables mean no relationship between them?
+4. What are the assumptions of linear regression? How do we make sure they are not violated?
+5. Explain the concept of Maximum Likelihood Estimator.
+6. Explain the algorithm of decision tree construction.
+7. What are advantages of random forests over decision trees?
+8. Given a time series data, how do you determine which time series model to use to forecast values?
+9. Explain 3-4 principles of telling a good story with data.
+10. What are measures and dimensions in Tableau?
+11. How do you choose a visualisation/chart to represent your data?
+12. What is the difference between a list and a tuple?
+13. Explain the difference between the long and wide formats in data.
+14. What are lambda functions in Python? How and when are they used?
+15. Explain the bias-variance tradeoff. 
+16. While building a machine learning model, how do you test if the model is overfitting?
+

dbQuery.py

@@ -0,0 +1,107 @@
+import pymysql
+#We will use connect() to connect to RDS Instance
+#host is the endpoint of your RDS instance
+#user is the username you have given while creating the RDS instance
+#Password is Master pass word you have given
+#db = pymysql.connect(host="database-gpt.cjdcshirzwmk.us-east-1.rds.amazonaws.com", user = "admin123", password="admin123", port=3306)
+# you have cursor instance here
+#cursor = db.cursor()
+def DB_SessionID():
+    
+    # cursor.execute("select version()")
+    db = pymysql.connect(host="gpt-database-new.cjdcshirzwmk.us-east-1.rds.amazonaws.com", user = "admin123", password="admin123", port=8080)
+    # you have cursor instance here
+    cursor = db.cursor()
+    db.select_db('gpt_interview_data')
+    
+    try:
+        # Begin the transaction
+        db.begin()
+        print("Connection sucessfull")
+        # Execute the SQL queries
+        cursor.execute('''INSERT INTO table1 (session_id, rating)
+                        SELECT t.count + 1, 5
+                        FROM (SELECT COUNT(*) AS count FROM table1) AS t;''')
+        cursor.execute("SELECT COUNT(*) AS new_length FROM table1")
+
+        # Commit the transaction
+        db.commit()
+        print("Transaction completed successfully")
+        session_id = cursor.fetchone()[0]
+        print(session_id)
+        return session_id
+
+    except Exception as e:
+        # Rollback the transaction if any error occurs
+        db.rollback()
+        print("Transaction failed")
+        print(e)
+        return 0
+
+    finally:
+        # Close the cursor and connection
+        cursor.close()
+        db.close()
+        
+
+
+def insertData(session_id, score, question_no):
+    db = pymysql.connect(host="gpt-database-new.cjdcshirzwmk.us-east-1.rds.amazonaws.com", user = "admin123", password="admin123", port=8080)
+    # you have cursor instance here
+    cursor = db.cursor()
+    db.select_db('gpt_interview_data')
+
+    score_obj_no = question_no - 1
+    question = score[score_obj_no].get("question")
+    response = score[score_obj_no].get("response")
+    grade = score[score_obj_no].get("score")
+    feedback = score[score_obj_no].get("feedback")
+    #code for pack-unpack
+    # Pack two 32-bit integers into a single 64-bit integer
+    sessionid_qno = packed = (int(session_id) << 32) | question_no
+    #directly formatting the string with variables is vulnerable to SQL injection attacks, so using parameterized queries instead of simple one.
+    sql2 = '''INSERT INTO table2 (session_id, session_question, question_num, question, response, feedback, score)
+VALUES (%s, %s, %s, %s, %s, %s, %s)'''
+#    sql2 = '''insert into table2 (session_id, session_question, question_num, question, response, feedback, score)
+#VALUES (2, 3564373, 3, "dsfds", "sdfgsdg", "sfdgg", 4)'''
+    print(type(session_id), session_id, type(sessionid_qno),sessionid_qno, type(question_no),question_no, type(question),question, type(response),response, type(feedback),feedback, type(grade),grade)
+    try:
+        # Begin the transaction
+        db.begin()
+        print("Connection sucessfull 2")
+        # Execute the SQL queries
+        cursor.execute(sql2,(session_id, sessionid_qno, question_no, question, response, feedback, grade))
+
+        # Commit the transaction
+        db.commit()
+
+        print("Transaction 2 completed successfully")
+        
+        return 1
+
+    except Exception as e:
+        # Rollback the transaction if any error occurs
+        db.rollback()
+        print(session_id)
+        print(score_obj_no)
+        print("Transaction 2 failed")
+        print(e)
+        return 0
+
+    finally:
+        # Close the cursor and connection
+        cursor.close()
+        db.close()
+
+
+
+
+# Unpack the integers from the packed value
+# a_unpacked = packed >> 32
+# b_unpacked = packed & 0xffffffff
+
+# print("a:", a)
+# print("b:", b)
+# print("packed:", packed)
+# print("a_unpacked:", a_unpacked)
+# print("b_unpacked:", b_unpacked)

requirements.txt

@@ -0,0 +1,5 @@
+openai
+re
+gradio
+pydub
+pymysql

scores.txt

@@ -0,0 +1,560 @@
+{
+interview question: can you briefly describe the difference between supervised and unsupervised learning techniques? provide one example of each.
+
+grade: 0,
+
+feedback: supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y can take continuous values (regression) or discrete values (classification). unsupervised learning is where you only have input data (x) and no corresponding output variable. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. examples of unsupervised learning are: association rule learning, clustering, and dimensionality reduction.
+}
+grade=0
+feedback=supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y can take continuous values (regression) or discrete values (classification). unsupervised learning is where you only have input data (x) and no corresponding output variable. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. examples of unsupervised learning are: association rule learning, clustering, and dimensionality reduction.
+
+.
+
+role:
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal is to map the input data to the correct output label. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. the goal is to find patterns in the data. an example of a supervised learning algorithm is linear regression and an example of an unsupervised learning algorithm is k-means clustering.
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal is to map the input data to the correct output label. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. the goal is to find patterns in the data. an example of a supervised learning algorithm is linear regression and an example of an unsupervised learning algorithm is k-means clustering.
+
+answer
+
+{
+interview question: can you briefly describe the difference between supervised and unsupervised learning techniques? provide one example of each.
+
+grade: 0,
+
+feedback: supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y, the output variable, can take one of two forms: it can be either a discrete value, like a class label, or a continuous value, like the price of a house. some examples of supervised learning are regression, decision trees, and support vector machines.
+
+unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called latent variables because they are not directly observable in the data. some examples of unsupervised
+grade=0
+feedback=supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y, the output variable, can take one of two forms: it can be either a discrete value, like a class label, or a continuous value, like the price of a house. some examples of supervised learning are regression, decision trees, and support vector machines.
+
+.
+
+grade: 0
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal of supervised learning is to approximate the mapping function so well that it can make predictions on unseen data. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. the goal of unsupervised learning is to find hidden structure in the data.
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal of supervised learning is to approximate the mapping function so well that it can make predictions on unseen data. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. the goal of unsupervised learning is to find hidden structure in the data.
+
+{
+grade: 0,
+feedback: supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y, in this case, is a function of x. for example, you could use supervised learning to classify an email as spam or not spam. in this case, x would be a set of features extracted from the email like the number of words, the number of all-caps words, whether or not �viagra� is in the email body, and y would be a label of spam or not spam.
+
+unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called unsupervised learning because unlike supervised learning above there is no correct
+grade=0
+feedback=supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. y, in this case, is a function of x. for example, you could use supervised learning to classify an email as spam or not spam. in this case, x would be a set of features extracted from the email like the number of words, the number of all-caps words, whether or not �viagra� is in the email body, and y would be a label of spam or not spam.
+
+one.
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. this mapping is represented as a model. the goal is to approximate the mapping function so well that when you have new input data (x), you can predict the output variables (y) for that data. supervised learning problems can be either regression or classification problems. for example, predicting the price of a house given a set of features is a regression problem whereas predicting whether a customer will churn or not is a classification problem. unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called unsupervised learning because unlike supervised learning
+grade=0
+feedback=your answer is incorrect. supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. this mapping is represented as a model. the goal is to approximate the mapping function so well that when you have new input data (x), you can predict the output variables (y) for that data. supervised learning problems can be either regression or classification problems. for example, predicting the price of a house given a set of features is a regression problem whereas predicting whether a customer will churn or not is a classification problem. unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called unsupervised learning because unlike supervised learning
+
+{
+grade: 0,
+feedback: im sorry, i dont understand what youre trying to say.
+}
+grade=0
+feedback=im sorry, i dont understand what youre trying to say.
+
+answer
+
+{
+    grade: 0,
+    feedback: your answer is incorrect. supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. this mapping function can be represented as y = f(x). common examples of supervised learning are regression and classification. unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called latent variables. a latent variable is a variable that is not observed but is rather inferred through another set of variables. an example of unsupervised learning is clustering.
+}
+grade=0
+feedback=your answer is incorrect. supervised learning is where you have input variables (x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. this mapping function can be represented as y = f(x). common examples of supervised learning are regression and classification. unsupervised learning is where you only have input data (x) and no corresponding output variables. the goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. these are called latent variables. a latent variable is a variable that is not observed but is rather inferred through another set of variables. an example of unsupervised learning is clustering.
+
+{
+    grade: 0,
+    feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the training data consists of a set of training examples, each of which includes the desired output value (also called the supervisory signal) for that instance. an unsupervised learning algorithm is used to learn the structure of the input data without using any labeled training data. a common example of unsupervised learning is clustering, which is used to group similar instances together. another example is dimensionality reduction, which is used to reduce the number of features in the data.
+}
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the training data consists of a set of training examples, each of which includes the desired output value (also called the supervisory signal) for that instance. an unsupervised learning algorithm is used to learn the structure of the input data without using any labeled training data. a common example of unsupervised learning is clustering, which is used to group similar instances together. another example is dimensionality reduction, which is used to reduce the number of features in the data.
+
+question
+
+{
+grade: 0,
+
+feedback: this question is asking for a brief description of the difference between supervised and unsupervised learning techniques, as well as one example of each. supervised learning is a type of machine learning where the model is trained using labeled data, meaning that there is a known correct output for each input. unsupervised learning is a type of machine learning where the model is trained using unlabeled data, meaning that there is no known correct output for each input. a common example of supervised learning is regression, where the goal is to predict a continuous value. a common example of unsupervised learning is clustering, where the goal is to group similar data points together.
+grade=0
+feedback=this question is asking for a brief description of the difference between supervised and unsupervised learning techniques, as well as one example of each. supervised learning is a type of machine learning where the model is trained using labeled data, meaning that there is a known correct output for each input. unsupervised learning is a type of machine learning where the model is trained using unlabeled data, meaning that there is no known correct output for each input. a common example of supervised learning is regression, where the goal is to predict a continuous value. a common example of unsupervised learning is clustering, where the goal is to group similar data points together.
+
+answer.
+
+{
+    grade: 0,
+    feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function that maps an input to an output based on example input-output pairs. this mapping is learned by training the supervised learning algorithm on a labeled dataset, which consists of a set of training examples. in contrast, unsupervised learning is a type of machine learning algorithm that is used to learn a function that maps an input to an output without using any labeled training data. one example of a supervised learning algorithm is linear regression while one example of an unsupervised learning algorithm is k-means clustering.
+}
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function that maps an input to an output based on example input-output pairs. this mapping is learned by training the supervised learning algorithm on a labeled dataset, which consists of a set of training examples. in contrast, unsupervised learning is a type of machine learning algorithm that is used to learn a function that maps an input to an output without using any labeled training data. one example of a supervised learning algorithm is linear regression while one example of an unsupervised learning algorithm is k-means clustering.
+
+{
+grade: 0, 
+feedback: supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. the training dataset contains the correct answers (also called labels). the machine learning algorithm uses the training dataset to learn how to make predictions. once the machine learning algorithm has been trained, it can be used to make predictions on new data (called the test dataset). the predictions made by the machine learning algorithm can be compared to the labels in the test dataset to see how accurate the predictions are.
+
+unsupervised learning is a type of machine learning algorithm that does not use a training dataset. instead, the machine learning algorithm is given a dataset and it is left to figure out how to make predictions on new data. one example of unsupervised learning is clustering. clustering is a type of unsupervised learning that groups data points together based on similarities. another example of unsupervised learning
+grade=0
+feedback=supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. the training dataset contains the correct answers (also called labels). the machine learning algorithm uses the training dataset to learn how to make predictions. once the machine learning algorithm has been trained, it can be used to make predictions on new data (called the test dataset). the predictions made by the machine learning algorithm can be compared to the labels in the test dataset to see how accurate the predictions are.
+
+question
+
+{
+grade: 0,
+feedback: supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal of supervised learning is to approximate the mapping function so well that it can predict the correct output for any unseen instance. this requires that the training data be labeled with correct answers. some examples of supervised learning are regression, decision trees, and support vector machines. 
+
+unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. the goal of unsupervised learning is to find hidden patterns or intrinsic structures in data. some examples of unsupervised learning are clustering and dimensionality reduction.
+}
+grade=0
+feedback=supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. the goal of supervised learning is to approximate the mapping function so well that it can predict the correct output for any unseen instance. this requires that the training data be labeled with correct answers. some examples of supervised learning are regression, decision trees, and support vector machines. 
+
+answer
+
+grade: 0
+
+feedback:
+
+supervised learning is a type of machine learning where the model is trained on a labeled dataset, i.e. the target variable is known. the model is then used to predict the target variable for new data. an example of a supervised learning algorithm is linear regression. 
+
+unsupervised learning is a type of machine learning where the model is trained on an unlabeled dataset, i.e. the target variable is not known. the model is then used to find patterns and relationships in the data. an example of an unsupervised learning algorithm is k-means clustering.
+grade=0
+feedback=No feedback provided for this response.
+
+{
+grade: 0,
+feedback: you need to provide a response to the question. this question is about the concept of multicollinearity in linear regression and how it is used.
+grade=0
+feedback=you need to provide a response to the question. this question is about the concept of multicollinearity in linear regression and how it is used.
+
+{
+    grade: 0,
+    feedback: your answer is incorrect. supervised learning is where you have a target or dependent variable that you are trying to predict from a set of independent variables. linear regression is an example of a supervised learning algorithm. unsupervised learning is where you only have independent variables and no dependent variable. k-means clustering is an example of an unsupervised learning algorithm.
+}
+grade=0
+feedback=your answer is incorrect. supervised learning is where you have a target or dependent variable that you are trying to predict from a set of independent variables. linear regression is an example of a supervised learning algorithm. unsupervised learning is where you only have independent variables and no dependent variable. k-means clustering is an example of an unsupervised learning algorithm.
+
+answer
+
+{
+grade: 0,
+feedback: supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. the training dataset contains the correct answers (also called labels). the machine learning algorithm uses the training dataset to learn how to make predictions. once the machine learning algorithm has learned how to make predictions, it can be used to make predictions on new data (called the test dataset). unsupervised learning is a type of machine learning algorithm that does not use a training dataset. instead, the machine learning algorithm tries to find patterns in the data. one example of supervised learning is regression. one example of unsupervised learning is clustering.
+grade=0
+feedback=supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. the training dataset contains the correct answers (also called labels). the machine learning algorithm uses the training dataset to learn how to make predictions. once the machine learning algorithm has learned how to make predictions, it can be used to make predictions on new data (called the test dataset). unsupervised learning is a type of machine learning algorithm that does not use a training dataset. instead, the machine learning algorithm tries to find patterns in the data. one example of supervised learning is regression. one example of unsupervised learning is clustering.
+
+a good question for me.
+
+grade: 0
+
+feedback: im sorry to hear that you dont think this is a good question for you. can you please provide a response anyway?
+grade=0
+feedback=im sorry to hear that you dont think this is a good question for you. can you please provide a response anyway?
+
+answer
+
+{
+
+grade: 0,
+
+feedback: you did not attempt to answer the question.
+grade=0
+feedback=you did not attempt to answer the question.
+
+. i would like to hear more about unsupervised learning.
+
+{
+grade: 7,
+feedback: you provided a good overview of supervised and unsupervised learning. for unsupervised learning, you could mention a few more examples, such as identifying customer segments in retail data, or detecting fraudulent activity in financial data. you could also mention a few more methods of unsupervised learning, such as clustering and association rule mining.
+grade=7
+feedback=you provided a good overview of supervised and unsupervised learning. for unsupervised learning, you could mention a few more examples, such as identifying customer segments in retail data, or detecting fraudulent activity in financial data. you could also mention a few more methods of unsupervised learning, such as clustering and association rule mining.
+
+<br>
+<br>
+recall measures how well the model predicts positive cases, i.e., how many of the actual positive cases are predicted as positive by the model. it is defined as the ratio of true positives (tp) to the total number of actual positive cases (tp + false negatives, fn).<br>
+<br>
+the relationship between precision and recall is such that an increase in precision can lead to a decrease in recall and vice versa. this is because precision is a measure of how many of the positive predictions are actually true positives, while recall is a measure of how many of the actual positive cases are predicted as positive.
+
+grade: 7-8
+
+feedback:
+
+your answer is factually correct and you explained it with an example explaining accuracys shortcomings as a measure. to make your answer better, you should also give another example where accuracy is not a good measure for a problem, and to give a better impression,
+grade=7
+feedback=No feedback provided for this response.
+
+, can you please repeat the question?
+
+{
+interview question: can you briefly describe the difference between supervised and unsupervised learning techniques? provide one example of each.
+
+grade: 0
+
+feedback: the candidate could not answer the question.
+grade=0
+feedback=the candidate could not answer the question.
+
+
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+supervised learning is a type of machine learning algorithm that is used to learn a mapping function from input data to desired output labels. the algorithm learns this mapping function by looking at a training dataset of input/output pairs and making predictions on new input data.
+
+unsupervised learning is a type of machine learning algorithm that is used to learn hidden patterns or structures in data. the algorithm does this by making predictions on new data without any training labels.
+grade=0
+feedback=No feedback provided for this response.
+
+{
+grade: 0,
+feedback: the candidate has not attempted to answer the question.
+grade=0
+feedback=the candidate has not attempted to answer the question.
+
+{
+grade: 0,
+feedback: your answer is incorrect. you should provide a response that discusses different types of regularization techniques and how they work.
+grade=0
+feedback=your answer is incorrect. you should provide a response that discusses different types of regularization techniques and how they work.
+
+grade: 0
+
+feedback: your answer is incorrect. regularization techniques are used to prevent overfitting in machine learning models.
+grade=0
+feedback=your answer is incorrect. regularization techniques are used to prevent overfitting in machine learning models.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the data is labeled and the algorithm is trained to learn from this data. unsupervised learning is a type of machine learning where the data is not labeled and the algorithm is trained to learn from this data.
+grade=0
+feedback=No feedback provided for this response.
+
+the tags help in the prediction of the output. for example, if we want to predict whether the person will like the movie or not, we can use the supervised learning technique. the data set will contain two values, one for the person who liked the movie and one for the person who did not like the movie.
+
+unsupervised learning is the learning where the data sets are not tagged. the tags are not used for the prediction of output. for example, if we want to segregate the data set into different groups, we can use the unsupervised learning technique.
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback: your answer is incorrect. in supervised learning, we have labeled data which means that we have both input features and output labels, and the algorithm learns to map the input features to the output label. can you provide an example of a supervised learning problem?
+grade=0
+feedback=your answer is incorrect. in supervised learning, we have labeled data which means that we have both input features and output labels, and the algorithm learns to map the input features to the output label. can you provide an example of a supervised learning problem?
+
+this means that there is no teacher to guide the learning process. the aim in unsupervised learning is to find hidden patterns or intrinsic structures in the data.
+
+grade: 7-8
+
+feedback: while your answer is technically correct, it can be improved by providing a more detailed explanation of what unsupervised learning is and how it works. for example, you can mention that unsupervised learning algorithms are used to cluster data points together based on similarity, and that they are used to find patterns in data that are not easily observable.
+grade=7
+feedback=while your answer is technically correct, it can be improved by providing a more detailed explanation of what unsupervised learning is and how it works. for example, you can mention that unsupervised learning algorithms are used to cluster data points together based on similarity, and that they are used to find patterns in data that are not easily observable.
+
+grade: 9-10
+
+feedback: your answer is correct and you provided a real-world example of unsupervised learning.
+grade=9
+feedback=your answer is correct and you provided a real-world example of unsupervised learning.
+
+{
+grade: 0,
+feedback: you are not ready for the next question.
+}
+grade=0
+feedback=you are not ready for the next question.
+
+
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback: your answer in incorrect. precision and recall are both evaluation metrics for classification models. precision measures the number of correct positive predictions out of all positive predictions while recall measures the number of correct positive predictions out of all actual positive values. ideally, you should provide an example of when you would use precision over recall and vice versa.
+grade=0
+feedback=your answer in incorrect. precision and recall are both evaluation metrics for classification models. precision measures the number of correct positive predictions out of all positive predictions while recall measures the number of correct positive predictions out of all actual positive values. ideally, you should provide an example of when you would use precision over recall and vice versa.
+
+.
+
+{
+grade: 0,
+feedback: the answer is incorrect. you should provide a detailed explanation of how you would handle missing values in a dataset. some of the ways in which you can handle missing values are:
+
+we can replace or impute the missing values with the mean or median for numeric data and mode for categorical data. essentially, we replace the missing values with measures of the central tendency for each column in the dataset.  
+another way in which we can handle missing values in categorical data is by creating another level i.e. another unique value which replaces all the missing values in that column. 
+
+we can also run predictive models which can impute missing data. here, we treat the column with missing values as the target column. the training data will be the rows in which the target column has values and the test data will comprise of the rows where the target column does not have values. we can now build a model using
+grade=0
+feedback=the answer is incorrect. you should provide a detailed explanation of how you would handle missing values in a dataset. some of the ways in which you can handle missing values are:
+
+supervised learning is where you have input variables x and an output variable y and you use an algorithm to learn the mapping function from the input to the output. y, the output variable, can take on continuous values if you are doing regression or discrete values if you are doing classification. an example of supervised learning would be regression where you are trying to predict the price of a house given a set of features about the house. the features would be the input variables x and the price of the house would be the output variable y that you are trying to predict.
+
+unsupervised learning is where you only have input data x and no corresponding output variables. you are trying to learn some structure or correlation in the data in order to do some type of prediction or analysis. a common example of unsupervised learning is clustering where you are trying to group similar data points together. another example might be dimensionality reduction where you are trying to find new ways to represent your data that are
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the model is trained on a labeled dataset, i.e. the target variable is known. the model is then used to predict the target variable for new data. unsupervised learning is a type of machine learning where the model is trained on an unlabeled dataset, i.e. the target variable is not known. the model is then used to find patterns in the data. an example of supervised learning would be training a model to predict whether or not a patient has cancer based on a dataset of patients with known outcomes. an example of unsupervised learning would be training a model to cluster patients based on their medical history.
+grade=0
+feedback=No feedback provided for this response.
+
+i should have studied more.
+
+grade: 0
+
+feedback:
+
+this answer is incorrect. supervised learning is a type of machine learning where the data is labeled and the algorithm is trained to learn from this data. unsupervised learning is a type of machine learning where the data is not labeled and the algorithm is trained to learn from this data.
+grade=0
+feedback=No feedback provided for this response.
+
+व्यवस्थापित सीखने के लिए आपको स्पष्ट मार्गदर्शन की आवश्यकता होती है। यदि आपको एक निर्देशक नहीं है, तो आप कोई भी सीख नहीं सकते
+grade=None
+feedback=No feedback provided for this response.
+
+एक ब्लॉक के लिए एक वाक्य निर्दिष्ट करें। आपके पास एक नोट है जो आपके पास पहले से ही है। आपके पास एक मुख्य वाक्य है जो सीधे आपके पास नह
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback: please provide an answer to the question.
+grade=0
+feedback=please provide an answer to the question.
+
+!
+
+role:
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that uses a labeled dataset to train a model to make predictions, while unsupervised learning is a type of machine learning algorithm that uses an unlabeled dataset to train a model to make predictions. you should provide an example of each to exhaustively answer the question at hand.
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that uses a labeled dataset to train a model to make predictions, while unsupervised learning is a type of machine learning algorithm that uses an unlabeled dataset to train a model to make predictions. you should provide an example of each to exhaustively answer the question at hand.
+
+grade: 0
+
+feedback:
+
+the candidate does not provide a response to the question.
+grade=0
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+this response is not relevant to the question. supervised learning is a type of machine learning where the model is trained using labeled data, i.e. data where the target variable is known. the model is then used to predict the target variable for new, unlabeled data. an example of a supervised learning algorithm is linear regression. unsupervised learning is a type of machine learning where the model is trained using unlabeled data, i.e. data where the target variable is not known. the model is then used to cluster data points or find patterns in the data. an example of an unsupervised learning algorithm is k-means clustering.
+grade=0
+feedback=No feedback provided for this response.
+
+{
+grade: 0,
+feedback: the user did not provide a response to the question.
+grade=0
+feedback=the user did not provide a response to the question.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the data is labeled and the algorithm is trained to learn from this labeled data. unsupervised learning is a type of machine learning where the data is not labeled and the algorithm is trained to learn from this unlabeled data.
+grade=0
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+this response is not acceptable. please provide a brief description of the difference between supervised and unsupervised learning techniques, along with one example of each.
+grade=0
+feedback=No feedback provided for this response.
+
+{
+grade: 0,
+feedback: your answer in incorrect. you should provide a response that explains the difference between supervised and unsupervised learning techniques, and provide one example of each.
+}
+grade=0
+feedback=your answer in incorrect. you should provide a response that explains the difference between supervised and unsupervised learning techniques, and provide one example of each.
+
+grade: 0
+
+feedback: your answer in incorrect. please describe the difference between supervised and unsupervised learning techniques and provide one example of each.
+grade=0
+feedback=your answer in incorrect. please describe the difference between supervised and unsupervised learning techniques and provide one example of each.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the model is trained using labeled data, i.e. data where the correct output is known. the model is then able to generalize to new data. unsupervised learning is a type of machine learning where the model is trained using unlabeled data, i.e. data where the correct output is not known. the model is then able to find patterns in the data.
+grade=0
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the training data is labelled, and the goal is to learn a function that can map the input data to the corresponding labels. unsupervised learning is a type of machine learning where the training data is not labelled, and the goal is to learn a function that can map the input data to some structure or pattern.
+grade=0
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, i.e. the input data is labeled with the correct output. this means that the algorithm knows the right answer for a given input. unsupervised learning is a type of machine learning where the algorithm is not given any labels and has to learn from the data itself. an example of supervised learning would be training a machine learning algorithm on a dataset of images that are labeled as “cat” or “not cat”. an example of unsupervised learning would be training a machine learning algorithm on a dataset of images where the algorithm has to learn to identify patterns on its own.
+grade=0
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the model is trained using labeled data, i.e. data with known outcomes. the model is then used to predict the outcome for new data. unsupervised learning is a type of machine learning where the model is trained using data that is not labeled, i.e. data with unknown outcomes. the model is then used to find patterns and relationships in the data. an example of supervised learning would be training a model to predict whether or not a patient has a disease, based on a set of labeled data. an example of unsupervised learning would be training a model to cluster data points into groups, based on their similarities.
+grade=0
+feedback=No feedback provided for this response.
+
+ini. supervised learning is where we have a training dataset with the correct answers, and we use that to train our model. unsupervised learning is where we only have a dataset, and we try to find patterns in the data.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is where we have a training dataset with the correct answers, and we use that to train our model. unsupervised learning is where we only have a dataset, and we try to find patterns in the data.
+grade=0
+feedback=No feedback provided for this response.
+
+role:
+
+the candidate has not provided a response to the question.
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback: the user did not provide a response to the question.
+grade=0
+feedback=the user did not provide a response to the question.
+
+my name is john. i am a data analyst.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is a type of machine learning where the training data is labeled, and the model is trained to predict the labels. unsupervised learning is a type of machine learning where the training data is not labeled, and the model is trained to find patterns in the data.
+grade=0
+feedback=No feedback provided for this response.
+
+role:
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning where the data is labeled and the algorithm is trained to learn from this data. unsupervised learning is a type of machine learning where the data is not labeled and the algorithm is trained to learn from this data.
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning where the data is labeled and the algorithm is trained to learn from this data. unsupervised learning is a type of machine learning where the data is not labeled and the algorithm is trained to learn from this data.
+
+hello
+
+{
+grade: 0,
+feedback: you did not answer the question. please try again.
+}
+grade=0
+feedback=you did not answer the question. please try again.
+
+role: {grade: 0, feedback: the candidate did not attempt to answer the question.}
+grade=0
+feedback=the candidate did not attempt to answer the question.
+
+{grade: 0,
+feedback: the user has not provided a response to the question. the interview has ended abruptly.}
+grade=0
+feedback=the user has not provided a response to the question. the interview has ended abruptly.
+
+! supervised learning is a technique in which the model is trained using labeled data, meaning that there is a known output for every input. a common example of supervised learning is regression, in which the goal is to predict a continuous value. unsupervised learning is a technique in which the model is trained using unlabeled data, meaning that there is no known output for the given inputs. a common example of unsupervised learning is clustering, in which the goal is to group similar data points together.
+
+grade: 9-10
+
+feedback:
+
+your answer is technically correct and provides a clear explanation of the difference between supervised and unsupervised learning techniques. you also provide a real-world example of each technique. well done!
+grade=9
+feedback=No feedback provided for this response.
+
+role:
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. you should provide a real-world example of each to better explain the concept.
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn a function from labeled training data. unsupervised learning is a type of machine learning algorithm that is used to learn a function from unlabeled training data. you should provide a real-world example of each to better explain the concept.
+
+the machine learning algorithm learn from this dataset and try to predict the output for the new dataset. for example, we have a dataset of student and whether they are pass or fail. we provide this dataset to the machine learning algorithm. the algorithm learn from the dataset and try to predict whether the new student will pass or fail.
+
+unsupervised learning is where we provide the machine learning algorithm with unlabelled dataset. the machine learning algorithm try to find the relationship between the data. for example, we have a dataset of student and their marks. we provide this dataset to the machine learning algorithm. the algorithm try to find the relationship between the marks and try to find the groups of student.
+
+{
+    grade: 7,
+    feedback: your answer is technically correct but it lacks clarity of explanation. you could improve your answer by providing a better example of unsupervised learning. for example, you can mention how unsupervised learning can be used to
+grade=7
+feedback=your answer is technically correct but it lacks clarity of explanation. you could improve your answer by providing a better example of unsupervised learning. for example, you can mention how unsupervised learning can be used to
+
+, great. so, the difference between supervised and unsupervised learning, so supervised learning is where you have a target variable, and you’re using an algorithm to try and predict that target variable. so, a really good example of that would be, if you were trying to predict house prices, the target variable would be the house price, and you’d be using a variety of features to try and predict that. so, that would be a supervised learning problem.
+
+grade: 0
+
+feedback:
+
+your answer is incorrect. supervised learning is where you have a target variable and you use an algorithm to try and predict that target variable. unsupervised learning is where you dont have a target variable and you are trying to find patterns in the data. a good example of unsupervised learning would be clustering data points into different groups.
+grade=0
+feedback=No feedback provided for this response.
+
+yn ystod y cyfnod, roedd y cyfrifiadau’n ymgyrchu’n ddwys iawn, ac roedden nhw’n helpu i sicrhau bod y cyfrifiadau yn cael eu haddasu i’r gofynion sy’n deillio o’r amrywiaeth eang o broblemau sy’n deillio o’r amrywiaeth eang o broblemau sy’n deillio o’r amrywiaeth eang o broblemau.
+
+role:
+
+{
+
+grade: 0,
+
+feedback: your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn from labeled data, i.e. data that has been classified into different categories. unsuper
+grade=0
+feedback=your answer is incorrect. supervised learning is a type of machine learning algorithm that is used to learn from labeled data, i.e. data that has been classified into different categories. unsuper
+
+the data is split into training and testing. the model is trained with the training data. the model is tested with the testing data. the model is then tweaked to improve performance. the performance is measured with a performance metric. the model is then put into production.
+
+supervised learning: the data is labeled. the labels are the results of the training data.
+
+unsupervised learning: the data is not labeled. the labels are not the results of the training data.
+grade=None
+feedback=No feedback provided for this response.
+
+grade: 0
+
+feedback: the users response is incorrect and does not provide any additional information.
+grade=0
+feedback=the users response is incorrect and does not provide any additional information.
+

test_db.py

@@ -0,0 +1,46 @@
+import pymysql
+db = pymysql.connect(host="database-gpt.cjdcshirzwmk.us-east-1.rds.amazonaws.com", user="admin123", password="admin123", port=3306)
+cursor = db.cursor()
+
+# Use the correct database
+
+try:
+    db.select_db('gpt_interview_data')
+    print("Connection successful")
+except Exception as e:
+    print(e)
+    print("DB connection error")
+    exit(1)
+
+# Insert a new row into table1 and return the session ID
+sql_insert = '''
+START TRANSACTION;
+INSERT INTO table1 (session_id, rating)
+SELECT t.count + 1, 5
+FROM (SELECT COUNT(*) AS count FROM table1) AS t;
+
+SELECT COUNT(*) FROM table1;
+COMMIT;
+'''
+
+try:
+    cursor.execute(sql_insert)
+    session_id = cursor.fetchone()[0]
+    print("New session ID:", session_id)
+except Exception as e:
+    print(e)
+    print("Error inserting row")
+    exit(1)
+
+# Get the total number of rows in table1
+sql_count = "SELECT COUNT(*) FROM table1"
+try:
+    cursor.execute(sql_count)
+    count = cursor.fetchone()[0]
+    print("Total rows in table1:", count)
+except Exception as e:
+    print(e)
+    print("Error getting row count")
+    exit(1)
+
+db.close()

test_db2.py

@@ -0,0 +1,34 @@
+import pymysql
+#We will use connect() to connect to RDS Instance
+#host is the endpoint of your RDS instance
+#user is the username you have given while creating the RDS instance
+#Password is Master pass word you have given 
+db = pymysql.connect(host="database-gpt.cjdcshirzwmk.us-east-1.rds.amazonaws.com", user = "admin123", password="admin123", port=3306)
+# you have cursor instance here
+cursor = db.cursor()
+cursor.execute("select version()")
+#now you will get the version of MYSQL you have selected on instance
+data = cursor.fetchone()
+#Lets's create a DB
+# sql = '''create database kTestDb'''
+# cursor.execute(sql)
+# cursor.connection.commit()
+db.select_db("kTestDb")
+#Create a table 
+sql = '''
+create table person ( id int not null auto_increment,fname text, lname text, primary key (id) )'''
+cursor.execute(sql)
+#Check if our table is created or not 
+sql = '''show tables'''
+cursor.execute(sql)
+cursor.fetchall()
+#Output of above will be (('person',),)
+#Insert some records in the table 
+sql = ''' insert into person(fname, lname) values('%s', '%s')''' % ('XXX', 'YYY')
+cursor.execute(sql)
+db.commit()
+#Lets select the data from above added table
+sql = '''select * from person'''
+cursor.execute(sql)
+cursor.fetchall()
+#Output of above will be ((1, 'XXX', 'YYY'),)