diff --git a/14-Streamlit/app.py b/14-Streamlit/app.py
new file mode 100644
index 000000000..98b29a209
--- /dev/null
+++ b/14-Streamlit/app.py
@@ -0,0 +1,29 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+## Title of the aplication
+st.title("Hello Streamlit")
+
+## Diplay a Simple Text
+st.write("This is a imple text")
+
+##create a simple Dataframe
+
+df = pd.DataFrame({
+    'first column': [1, 2, 3, 4],
+    'second column': [10, 20, 30, 40]
+})
+
+
+## Display the Dataframe
+st.write("Here is the dataframe")
+st.write(df)
+
+
+##create a line chart
+
+chart_data=pd.DataFrame(
+    np.random.randn(20,3),columns=['a','b','c']
+)
+st.line_chart(chart_data)
\ No newline at end of file
diff --git a/14-Streamlit/classification.py b/14-Streamlit/classification.py
new file mode 100644
index 000000000..77a9c7203
--- /dev/null
+++ b/14-Streamlit/classification.py
@@ -0,0 +1,32 @@
+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+
+@st.cache_data
+def load_data():
+    iris = load_iris()
+    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+    df['species'] = iris.target
+    return df, iris.target_names
+
+df,target_names=load_data()
+
+model=RandomForestClassifier()
+model.fit(df.iloc[:,:-1],df['species'])
+
+st.sidebar.title("Input Features")
+sepal_length = st.sidebar.slider("Sepal length", float(df['sepal length (cm)'].min()), float(df['sepal length (cm)'].max()))
+sepal_width = st.sidebar.slider("Sepal width", float(df['sepal width (cm)'].min()), float(df['sepal width (cm)'].max()))
+petal_length = st.sidebar.slider("Petal length", float(df['petal length (cm)'].min()), float(df['petal length (cm)'].max()))
+petal_width = st.sidebar.slider("Petal width", float(df['petal width (cm)'].min()), float(df['petal width (cm)'].max()))
+
+input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
+
+## PRediction
+prediction = model.predict(input_data)
+predicted_species = target_names[prediction[0]]
+
+st.write("Prediction")
+st.write(f"The predicted species is: {predicted_species}")
+
diff --git a/14-Streamlit/sampledata.csv b/14-Streamlit/sampledata.csv
new file mode 100644
index 000000000..a43ecda13
--- /dev/null
+++ b/14-Streamlit/sampledata.csv
@@ -0,0 +1,5 @@
+,Name,Age,City
+0,John,28,New York
+1,Jane,24,Los Angeles
+2,Jake,35,Chicago
+3,Jill,40,Houston
diff --git a/14-Streamlit/streamlit.ipynb b/14-Streamlit/streamlit.ipynb
new file mode 100644
index 000000000..b87272155
--- /dev/null
+++ b/14-Streamlit/streamlit.ipynb
@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Introduction to Streamlit\n",
+    "Streamlit is an open-source app framework for Machine Learning and Data Science projects. It allows you to create beautiful web applications for your machine learning and data science projects with simple Python scripts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/14-Streamlit/widgets.py b/14-Streamlit/widgets.py
new file mode 100644
index 000000000..7508e7484
--- /dev/null
+++ b/14-Streamlit/widgets.py
@@ -0,0 +1,37 @@
+import streamlit as st
+import pandas as pd
+
+st.title("Streamlit Text Input")
+
+name=st.text_input("Enter your name:")
+
+
+age=st.slider("Select your age:",0,100,25)
+
+st.write(f"Your age is {age}.")
+
+options = ["Python", "Java", "C++", "JavaScript"]
+choice = st.selectbox("Choose your favorite language:", options)
+st.write(f"You selected {choice}.")
+
+if name:
+    st.write(f"Hello, {name}")
+
+
+data = {
+    "Name": ["John", "Jane", "Jake", "Jill"],
+    "Age": [28, 24, 35, 40],
+    "City": ["New York", "Los Angeles", "Chicago", "Houston"]
+}
+
+df = pd.DataFrame(data)
+df.to_csv("sampledata.csv")
+st.write(df)
+
+
+uploaded_file=st.file_uploader("Choose a CSV file",type="csv")
+
+if uploaded_file is not None:
+    df=pd.read_csv(uploaded_file)
+    st.write(df)
+
diff --git a/15-Memory Management/memory_manage.ipynb b/15-Memory Management/memory_manage.ipynb
new file mode 100644
index 000000000..c8a0f8d57
--- /dev/null
+++ b/15-Memory Management/memory_manage.ipynb	
@@ -0,0 +1,1119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python Memory Management\n",
+    "Memory management in Python involves a combination of automatic garbage collection, reference counting, and various internal optimizations to efficiently manage memory allocation and deallocation. Understanding these mechanisms can help developers write more efficient and robust applications.\n",
+    "\n",
+    "1. Key Concepts in Python Memory Management\n",
+    "2. Memory Allocation and Deallocation\n",
+    "3. Reference Counting\n",
+    "4. Garbage Collection\n",
+    "5. The gc Module\n",
+    "6. Memory Management Best Practices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reference Counting\n",
+    "Reference counting is the primary method Python uses to manage memory. Each object in Python maintains a count of references pointing to it. When the reference count drops to zero, the memory occupied by the object is deallocated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "a=[]\n",
+    "## 2 (one reference from 'a' and one from getrefcount())\n",
+    "print(sys.getrefcount(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "b=a\n",
+    "print(sys.getrefcount(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "del b\n",
+    "print(sys.getrefcount(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Garbage Collection\n",
+    "Python includes a cyclic garbage collector to handle reference cycles. Reference cycles occur when objects reference each other, preventing their reference counts from reaching zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gc\n",
+    "## enable garbage collection\n",
+    "gc.enable()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gc.disable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1036"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'collections': 182, 'collected': 1611, 'uncollectable': 0}, {'collections': 16, 'collected': 255, 'uncollectable': 0}, {'collections': 2, 'collected': 1036, 'uncollectable': 0}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Get garbage collection stats\n",
+    "print(gc.get_stats())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### get unreachable objects\n",
+    "print(gc.garbage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  Memory Management Best Practices\n",
+    "1. Use Local Variables: Local variables have a shorter lifespan and are freed sooner than global variables.\n",
+    "2. Avoid Circular References: Circular references can lead to memory leaks if not properly managed.\n",
+    "3. Use Generators: Generators produce items one at a time and only keep one item in memory at a time, making them memory efficient.\n",
+    "4. Explicitly Delete Objects: Use the del statement to delete variables and objects explicitly.\n",
+    "5. Profile Memory Usage: Use memory profiling tools like tracemalloc and memory_profiler to identify memory leaks and optimize memory usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object obj1 created\n",
+      "Object obj2 created\n",
+      "Object obj1 deleted\n",
+      "Object obj2 deleted\n",
+      "Object obj1 deleted\n",
+      "Object obj2 deleted\n",
+      "Object obj1 deleted\n",
+      "Object obj2 deleted\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "gc: collectable <NameError 0x00000205D63FEF80>\n",
+      "gc: collectable <traceback 0x00000205D69F5D80>\n",
+      "gc: collectable <traceback 0x00000205D69F7980>\n",
+      "gc: collectable <frame 0x00000205D646D240>\n",
+      "gc: collectable <function 0x00000205D64FFD80>\n",
+      "gc: collectable <frame 0x00000205D6453230>\n",
+      "gc: collectable <frame 0x00000205D6A10CA0>\n",
+      "gc: collectable <ExecutionResult 0x00000205D647D7F0>\n",
+      "gc: collectable <frame 0x00000205D64B07C0>\n",
+      "gc: collectable <list 0x00000205D6A03A00>\n",
+      "gc: collectable <list 0x00000205D6A00640>\n",
+      "gc: collectable <list 0x00000205D6A1C380>\n",
+      "gc: collectable <function 0x00000205D642BA60>\n",
+      "gc: collectable <list 0x00000205D6A03B80>\n",
+      "gc: collectable <Delete 0x00000205D69F6B90>\n",
+      "gc: collectable <Module 0x00000205D50EF310>\n",
+      "gc: collectable <ExecutionInfo 0x00000205D647E2D0>\n",
+      "gc: collectable <frame 0x00000205D629FC40>\n",
+      "gc: collectable <cell 0x00000205D647CC10>\n",
+      "gc: collectable <cell 0x00000205D647E3E0>\n",
+      "gc: collectable <function 0x00000205D64FFE20>\n",
+      "gc: collectable <Module 0x00000205D646B510>\n",
+      "gc: collectable <cell 0x00000205D647D930>\n",
+      "gc: collectable <Expr 0x00000205D69F4B90>\n",
+      "gc: collectable <tuple 0x00000205D6A242C0>\n",
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+      "gc: collectable <dict 0x00000205D69F5940>\n",
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+      "gc: collectable <coroutine 0x00000205D6941F80>\n",
+      "gc: collectable <tuple 0x00000205D6A1D280>\n",
+      "gc: collectable <dict 0x00000205D69F5640>\n",
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+      "gc: collectable <frame 0x00000205D641EA40>\n",
+      "gc: collectable <list 0x00000205D6A0D040>\n",
+      "gc: collectable <Call 0x00000205D69F7690>\n",
+      "gc: collectable <Name 0x00000205D69F4CD0>\n",
+      "gc: collectable <frame 0x00000205D6A41560>\n",
+      "gc: collectable <dict 0x00000205D69F5EC0>\n",
+      "gc: collectable <dict 0x00000205D6994900>\n",
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+      "gc: collectable <list 0x00000205D6A040C0>\n",
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+      "gc: collectable <method 0x00000205D69F6740>\n",
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+      "gc: collectable <dict 0x00000205D69F5D00>\n",
+      "gc: collectable <Call 0x00000205D69F7810>\n",
+      "gc: collectable <frame 0x00000205D64AAEA0>\n",
+      "gc: collectable <list 0x00000205D64A00C0>\n",
+      "gc: collectable <dict 0x00000205D69F6440>\n",
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+      "gc: collectable <dict 0x00000205D69FA140>\n",
+      "gc: collectable <dict 0x00000205D69F5500>\n",
+      "gc: collectable <list 0x00000205D6A06E40>\n",
+      "gc: collectable <list 0x00000205D6A07D40>\n",
+      "gc: collectable <dict 0x00000205D69F5E80>\n",
+      "gc: collectable <frame 0x00000205D6A3C5E0>\n",
+      "gc: collectable <list 0x00000205D64A0680>\n",
+      "gc: collectable <Attribute 0x00000205D69F5110>\n",
+      "gc: collectable <list 0x00000205D6A1EE40>\n",
+      "gc: collectable <list 0x00000205D6A019C0>\n",
+      "gc: collectable <frame 0x00000205D6A42A80>\n",
+      "gc: collectable <coroutine 0x00000205D641FA40>\n",
+      "gc: collectable <dict 0x00000205D69F5E00>\n",
+      "gc: collectable <Name 0x00000205D69F56D0>\n",
+      "gc: collectable <tuple 0x00000205D65C2350>\n",
+      "gc: collectable <Name 0x00000205D69F6590>\n",
+      "gc: collectable <dict 0x00000205D69F4F00>\n",
+      "gc: collectable <list 0x00000205D6488E80>\n",
+      "gc: collectable <dict 0x00000205D69F5BC0>\n",
+      "gc: collectable <Frame 0x00000205D62D9020>\n",
+      "gc: collectable <Frame 0x00000205D62D8F70>\n",
+      "gc: collectable <Frame 0x00000205D62D8EC0>\n",
+      "gc: collectable <Frame 0x00000205D62D8E10>\n",
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+      "gc: collectable <cell 0x00000205D65C0D00>\n",
+      "gc: collectable <function 0x00000205D6A607C0>\n",
+      "gc: collectable <StyleMeta 0x00000205D597C500>\n",
+      "gc: collectable <tuple 0x00000205D6B19670>\n",
+      "gc: collectable <Terminal256Formatter 0x00000205D65C2210>\n",
+      "gc: collectable <dict 0x00000205D6B25680>\n",
+      "gc: collectable <list 0x00000205D6B2E0C0>\n",
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+      "gc: collectable <FrameInfo 0x00000205D65EC8C0>\n",
+      "gc: collectable <tuple 0x00000205D65E9510>\n",
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+      "gc: collectable <list 0x00000205D6B1D000>\n",
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+      "gc: collectable <list 0x00000205D6B1D040>\n",
+      "gc: collectable <list 0x00000205D6B1D080>\n",
+      "gc: collectable <list 0x00000205D6B1D0C0>\n",
+      "gc: collectable <list 0x00000205D6B1D200>\n",
+      "gc: collectable <list 0x00000205D6B1D280>\n",
+      "gc: collectable <list 0x00000205D6B1D240>\n",
+      "gc: collectable <list 0x00000205D6B1D1C0>\n",
+      "gc: collectable <list 0x00000205D6B1D180>\n",
+      "gc: collectable <list 0x00000205D6B1D2C0>\n",
+      "gc: collectable <list 0x00000205D6B1FC40>\n",
+      "gc: collectable <list 0x00000205D6B1DB40>\n",
+      "gc: collectable <list 0x00000205D6B1E100>\n",
+      "gc: collectable <list 0x00000205D6B1D500>\n",
+      "gc: collectable <list 0x00000205D6B1E1C0>\n",
+      "gc: collectable <list 0x00000205D6B1DE80>\n",
+      "gc: collectable <list 0x00000205D6B1DF80>\n",
+      "gc: collectable <list 0x00000205D6B1E240>\n",
+      "gc: collectable <list 0x00000205D6B1D300>\n",
+      "gc: collectable <list 0x00000205D6B1D900>\n",
+      "gc: collectable <list 0x00000205D6B1E580>\n",
+      "gc: collectable <list 0x00000205D6B1E400>\n",
+      "gc: collectable <list 0x00000205D6B1E380>\n",
+      "gc: collectable <list 0x00000205D6B1E500>\n",
+      "gc: collectable <list 0x00000205D6B1E600>\n",
+      "gc: collectable <list 0x00000205D6B1E300>\n",
+      "gc: collectable <list 0x00000205D6B1E680>\n",
+      "gc: collectable <list 0x00000205D6B1E6C0>\n",
+      "gc: collectable <list 0x00000205D6B1E780>\n",
+      "gc: collectable <list 0x00000205D6B1E7C0>\n",
+      "gc: collectable <list 0x00000205D6B1E980>\n",
+      "gc: collectable <list 0x00000205D6B1C040>\n",
+      "gc: collectable <list 0x00000205D6B1D4C0>\n",
+      "gc: collectable <list 0x00000205D6B1DFC0>\n",
+      "gc: collectable <list 0x00000205D6B1C2C0>\n",
+      "gc: collectable <list 0x00000205D6B1E740>\n",
+      "gc: collectable <list 0x00000205D6B1E900>\n",
+      "gc: collectable <list 0x00000205D6B1E880>\n",
+      "gc: collectable <list 0x00000205D6B1E700>\n",
+      "gc: collectable <list 0x00000205D6B1DCC0>\n",
+      "gc: collectable <list 0x00000205D6B1ED40>\n",
+      "gc: collectable <list 0x00000205D6B1EB00>\n",
+      "gc: collectable <list 0x00000205D6B1EE80>\n",
+      "gc: collectable <list 0x00000205D6B1EF40>\n",
+      "gc: collectable <list 0x00000205D6B1F100>\n",
+      "gc: collectable <list 0x00000205D6B1F340>\n",
+      "gc: collectable <list 0x00000205D6B1F380>\n",
+      "gc: collectable <list 0x00000205D6B1F440>\n",
+      "gc: collectable <list 0x00000205D6B1E800>\n",
+      "gc: collectable <list 0x00000205D6B1F8C0>\n",
+      "gc: collectable <list 0x00000205D6B1D580>\n",
+      "gc: collectable <list 0x00000205D6B1FEC0>\n",
+      "gc: collectable <list 0x00000205D6B1E840>\n",
+      "gc: collectable <list 0x00000205D6B1ED80>\n",
+      "gc: collectable <list 0x00000205D6B1FBC0>\n",
+      "gc: collectable <list 0x00000205D6B1F540>\n",
+      "gc: collectable <list 0x00000205D6B1E5C0>\n",
+      "gc: collectable <list 0x00000205D6B1F940>\n",
+      "gc: collectable <list 0x00000205D6B2F680>\n",
+      "gc: collectable <list 0x00000205D6B2F640>\n",
+      "gc: collectable <list 0x00000205D6B2C080>\n",
+      "gc: collectable <list 0x00000205D6B2C100>\n",
+      "gc: collectable <list 0x00000205D6B2C1C0>\n",
+      "gc: collectable <list 0x00000205D6B2C240>\n",
+      "gc: collectable <list 0x00000205D6B2C2C0>\n",
+      "gc: collectable <list 0x00000205D6B2C340>\n",
+      "gc: collectable <list 0x00000205D6B2C3C0>\n",
+      "gc: collectable <list 0x00000205D6B2C440>\n",
+      "gc: collectable <list 0x00000205D6B2C180>\n",
+      "gc: collectable <list 0x00000205D6B2C640>\n",
+      "gc: collectable <list 0x00000205D6B2C4C0>\n",
+      "gc: collectable <list 0x00000205D6B2C7C0>\n",
+      "gc: collectable <list 0x00000205D6B2C6C0>\n",
+      "gc: collectable <list 0x00000205D6B2C740>\n",
+      "gc: collectable <list 0x00000205D6B2C940>\n",
+      "gc: collectable <list 0x00000205D6B2CA80>\n",
+      "gc: collectable <list 0x00000205D6B2CA00>\n",
+      "gc: collectable <list 0x00000205D6B2CB40>\n",
+      "gc: collectable <list 0x00000205D6B2CC40>\n",
+      "gc: collectable <list 0x00000205D6B2CD00>\n",
+      "gc: collectable <list 0x00000205D6B2CD80>\n",
+      "gc: collectable <list 0x00000205D6B2CF40>\n",
+      "gc: collectable <list 0x00000205D6B2CDC0>\n",
+      "gc: collectable <list 0x00000205D6B2CE40>\n",
+      "gc: collectable <list 0x00000205D6B2CEC0>\n",
+      "gc: collectable <list 0x00000205D6B2D180>\n",
+      "gc: collectable <list 0x00000205D6B2D200>\n",
+      "gc: collectable <list 0x00000205D6B2D080>\n",
+      "gc: collectable <list 0x00000205D6B2D2C0>\n",
+      "gc: collectable <list 0x00000205D6B2D340>\n",
+      "gc: collectable <list 0x00000205D6B2D4C0>\n",
+      "gc: collectable <list 0x00000205D6B2D440>\n",
+      "gc: collectable <list 0x00000205D6B2D3C0>\n",
+      "gc: collectable <list 0x00000205D6B2D580>\n",
+      "gc: collectable <list 0x00000205D6B2D6C0>\n",
+      "gc: collectable <list 0x00000205D6B2D640>\n",
+      "gc: collectable <list 0x00000205D6B2CE00>\n",
+      "gc: collectable <list 0x00000205D6B2C8C0>\n",
+      "gc: collectable <list 0x00000205D6B2D500>\n",
+      "gc: collectable <list 0x00000205D6B2D900>\n",
+      "gc: collectable <list 0x00000205D6B2DB40>\n",
+      "gc: collectable <list 0x00000205D6B2D980>\n",
+      "gc: collectable <list 0x00000205D6B2D800>\n",
+      "gc: collectable <list 0x00000205D6B2DAC0>\n",
+      "gc: collectable <list 0x00000205D6B2DC80>\n",
+      "gc: collectable <list 0x00000205D6B2DD40>\n",
+      "gc: collectable <list 0x00000205D6B2DDC0>\n",
+      "gc: collectable <list 0x00000205D6B2DE40>\n",
+      "gc: collectable <list 0x00000205D6B2DEC0>\n",
+      "gc: collectable <list 0x00000205D6B2DF40>\n",
+      "gc: collectable <list 0x00000205D6B2DA00>\n",
+      "gc: collectable <list 0x00000205D6B2D100>\n",
+      "gc: collectable <list 0x00000205D6B2E040>\n",
+      "gc: collectable <list 0x00000205D6B2D780>\n",
+      "gc: collectable <list 0x00000205D6B2E240>\n",
+      "gc: collectable <list 0x00000205D6B2CB80>\n",
+      "gc: collectable <list 0x00000205D6B2E300>\n",
+      "gc: collectable <list 0x00000205D6B2D9C0>\n",
+      "gc: collectable <tuple 0x00000205D65ED060>\n",
+      "gc: collectable <_ctypes.PyCArrayType 0x00000205D597D480>\n",
+      "gc: collectable <tuple 0x00000205D6B52520>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B1D640>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B62540>\n",
+      "gc: collectable <StgDict 0x00000205D6B3B370>\n",
+      "gc: collectable <tuple 0x00000205D65ED930>\n",
+      "gc: collectable <_ctypes.PyCArrayType 0x00000205D597B1A0>\n",
+      "gc: collectable <tuple 0x00000205D6B52160>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B620C0>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B62E00>\n",
+      "gc: collectable <StgDict 0x00000205D6B7A9B0>\n",
+      "gc: collectable <function 0x00000205D6B4DE40>\n",
+      "gc: collectable <function 0x00000205D6B4DEE0>\n",
+      "gc: collectable <tuple 0x00000205D65ED570>\n",
+      "gc: collectable <dict 0x00000205D6B61D00>\n",
+      "gc: collectable <type 0x00000205D597B580>\n",
+      "gc: collectable <tuple 0x00000205D6B629C0>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B59A80>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B5ACC0>\n",
+      "gc: collectable <MyObject 0x00000205D64A52E0>\n",
+      "gc: collectable <MyObject 0x00000205D65E8C80>\n",
+      "gc: collectable <MyObject 0x00000205D65E9AC0>\n",
+      "gc: collectable <MyObject 0x00000205D65E8EC0>\n",
+      "gc: collectable <dict 0x00000205D6B25500>\n",
+      "gc: collectable <list 0x00000205D6ADC100>\n",
+      "gc: collectable <list 0x00000205D6B48B80>\n",
+      "gc: collectable <list 0x00000205D6B48BC0>\n",
+      "gc: collectable <Line 0x00000205D647DC70>\n",
+      "gc: collectable <Line 0x00000205D647E240>\n",
+      "gc: collectable <Line 0x00000205D647D280>\n",
+      "gc: collectable <list 0x00000205D6B1DA00>\n",
+      "gc: collectable <cell 0x00000205D64A68F0>\n",
+      "gc: collectable <RegexLexerMeta 0x00000205D5977F40>\n",
+      "gc: collectable <tuple 0x00000205D6B1A610>\n",
+      "gc: collectable <list 0x00000205D6B26140>\n",
+      "gc: collectable <dict 0x00000205D6B49000>\n",
+      "gc: collectable <tuple 0x00000205D6B45EC0>\n",
+      "gc: collectable <tuple 0x00000205D6B45E80>\n",
+      "gc: collectable <tuple 0x00000205D6B49080>\n",
+      "gc: collectable <tuple 0x00000205D6B49040>\n",
+      "gc: collectable <tuple 0x00000205D6B490C0>\n",
+      "gc: collectable <tuple 0x00000205D6B49140>\n",
+      "gc: collectable <tuple 0x00000205D6B49180>\n",
+      "gc: collectable <list 0x00000205D6B48F80>\n",
+      "gc: collectable <tuple 0x00000205D6B49BC0>\n",
+      "gc: collectable <tuple 0x00000205D6B49540>\n",
+      "gc: collectable <list 0x00000205D6498E00>\n",
+      "gc: collectable <tuple 0x00000205D6B492C0>\n",
+      "gc: collectable <tuple 0x00000205D6B491C0>\n",
+      "gc: collectable <tuple 0x00000205D6B49240>\n",
+      "gc: collectable <tuple 0x00000205D6B49B80>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43100>\n",
+      "gc: collectable <tuple 0x00000205D6B49B40>\n",
+      "gc: collectable <list 0x00000205D6442D40>\n",
+      "gc: collectable <list 0x00000205D6B49300>\n",
+      "gc: collectable <list 0x00000205D6B48880>\n",
+      "gc: collectable <tuple 0x00000205D6B49440>\n",
+      "gc: collectable <tuple 0x00000205D6B49100>\n",
+      "gc: collectable <list 0x00000205D6B49340>\n",
+      "gc: collectable <tuple 0x00000205D6B49280>\n",
+      "gc: collectable <list 0x00000205D6B49600>\n",
+      "gc: collectable <tuple 0x00000205D6B49380>\n",
+      "gc: collectable <tuple 0x00000205D6B49900>\n",
+      "gc: collectable <tuple 0x00000205D6B49940>\n",
+      "gc: collectable <tuple 0x00000205D6B49A40>\n",
+      "gc: collectable <tuple 0x00000205D6B49A80>\n",
+      "gc: collectable <tuple 0x00000205D6B49D80>\n",
+      "gc: collectable <list 0x00000205D6B49640>\n",
+      "gc: collectable <list 0x00000205D6B48C00>\n",
+      "gc: collectable <tuple 0x00000205D6B49E40>\n",
+      "gc: collectable <list 0x00000205D6B498C0>\n",
+      "gc: collectable <tuple 0x00000205D6B49E80>\n",
+      "gc: collectable <tuple 0x00000205D6B4A0C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4A100>\n",
+      "gc: collectable <tuple 0x00000205D6B4A140>\n",
+      "gc: collectable <tuple 0x00000205D6B4A240>\n",
+      "gc: collectable <tuple 0x00000205D6B4A340>\n",
+      "gc: collectable <list 0x00000205D6B49840>\n",
+      "gc: collectable <list 0x00000205D6B49680>\n",
+      "gc: collectable <tuple 0x00000205D6B4A380>\n",
+      "gc: collectable <tuple 0x00000205D6A1D300>\n",
+      "gc: collectable <tuple 0x00000205D6B4A3C0>\n",
+      "gc: collectable <list 0x00000205D6B496C0>\n",
+      "gc: collectable <list 0x00000205D6B48D80>\n",
+      "gc: collectable <tuple 0x00000205D6B4A500>\n",
+      "gc: collectable <tuple 0x00000205D6B4A4C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4A580>\n",
+      "gc: collectable <list 0x00000205D6B49800>\n",
+      "gc: collectable <list 0x00000205D6B49880>\n",
+      "gc: collectable <list 0x00000205D6B48B40>\n",
+      "gc: collectable <tuple 0x00000205D6B4A680>\n",
+      "gc: collectable <list 0x00000205D6B49980>\n",
+      "gc: collectable <tuple 0x00000205D6B4A6C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4A780>\n",
+      "gc: collectable <list 0x00000205D6B499C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4A9C0>\n",
+      "gc: collectable <list 0x00000205D6B48AC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4AB00>\n",
+      "gc: collectable <list 0x00000205D6B49AC0>\n",
+      "gc: collectable <tuple 0x00000205D644C400>\n",
+      "gc: collectable <tuple 0x00000205D6B4AAC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4A840>\n",
+      "gc: collectable <tuple 0x00000205D6B4AA80>\n",
+      "gc: collectable <tuple 0x00000205D6B4AB40>\n",
+      "gc: collectable <list 0x00000205D6B49B00>\n",
+      "gc: collectable <list 0x00000205D6B49400>\n",
+      "gc: collectable <tuple 0x00000205D6B4AC40>\n",
+      "gc: collectable <list 0x00000205D6B49EC0>\n",
+      "gc: collectable <tuple 0x00000205D6B2BFC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4AC80>\n",
+      "gc: collectable <tuple 0x00000205D6B4AE00>\n",
+      "gc: collectable <tuple 0x00000205D6B4AF80>\n",
+      "gc: collectable <tuple 0x00000205D6B4AEC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4AFC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B000>\n",
+      "gc: collectable <list 0x00000205D6B49F00>\n",
+      "gc: collectable <list 0x00000205D6B48E40>\n",
+      "gc: collectable <tuple 0x00000205D6B4B040>\n",
+      "gc: collectable <list 0x00000205D6B4A180>\n",
+      "gc: collectable <tuple 0x00000205D6A01480>\n",
+      "gc: collectable <tuple 0x00000205D6B4B0C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B100>\n",
+      "gc: collectable <tuple 0x00000205D6B4B140>\n",
+      "gc: collectable <tuple 0x00000205D6B4B180>\n",
+      "gc: collectable <tuple 0x00000205D6B4B200>\n",
+      "gc: collectable <tuple 0x00000205D6B4B240>\n",
+      "gc: collectable <list 0x00000205D6B4A1C0>\n",
+      "gc: collectable <list 0x00000205D6B48CC0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B280>\n",
+      "gc: collectable <tuple 0x00000205D6B4B2C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B340>\n",
+      "gc: collectable <list 0x00000205D6B4A2C0>\n",
+      "gc: collectable <list 0x00000205D6B4A300>\n",
+      "gc: collectable <tuple 0x00000205D6B4B300>\n",
+      "gc: collectable <tuple 0x00000205D6B2BD40>\n",
+      "gc: collectable <tuple 0x00000205D6B4B3C0>\n",
+      "gc: collectable <list 0x00000205D6B49C80>\n",
+      "gc: collectable <tuple 0x00000205D6B4B380>\n",
+      "gc: collectable <list 0x00000205D6B4A400>\n",
+      "gc: collectable <tuple 0x00000205D6B4B440>\n",
+      "gc: collectable <list 0x00000205D6B4A440>\n",
+      "gc: collectable <tuple 0x00000205D6B4B4C0>\n",
+      "gc: collectable <list 0x00000205D6B4A080>\n",
+      "gc: collectable <tuple 0x00000205D6B4B500>\n",
+      "gc: collectable <tuple 0x00000205D6B4B580>\n",
+      "gc: collectable <list 0x00000205D6B4A540>\n",
+      "gc: collectable <tuple 0x00000205D69F7A80>\n",
+      "gc: collectable <tuple 0x00000205D6B4B5C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B600>\n",
+      "gc: collectable <tuple 0x00000205D6B4B640>\n",
+      "gc: collectable <tuple 0x00000205D6B4B680>\n",
+      "gc: collectable <tuple 0x00000205D6B4B700>\n",
+      "gc: collectable <tuple 0x00000205D6B4B740>\n",
+      "gc: collectable <tuple 0x00000205D6B4B780>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50680>\n",
+      "gc: collectable <tuple 0x00000205D6B4B7C0>\n",
+      "gc: collectable <list 0x00000205D6B4A5C0>\n",
+      "gc: collectable <tuple 0x00000205D6B540C0>\n",
+      "gc: collectable <tuple 0x00000205D6B4B940>\n",
+      "gc: collectable <list 0x00000205D6B4A040>\n",
+      "gc: collectable <Executing 0x00000205D64A7D70>\n",
+      "gc: collectable <dict 0x00000205D6489E00>\n",
+      "gc: collectable <tuple 0x00000205D65E9270>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42A70>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42AC0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42B60>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42BB0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42C00>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42C50>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42CA0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42DE0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42FC0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B42F70>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43010>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43060>\n",
+      "gc: collectable <builtin_method 0x00000205D6B430B0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B431A0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B431F0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43240>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43290>\n",
+      "gc: collectable <builtin_method 0x00000205D6B432E0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43330>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43380>\n",
+      "gc: collectable <builtin_method 0x00000205D6B433D0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43150>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43470>\n",
+      "gc: collectable <builtin_method 0x00000205D6B434C0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43510>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43560>\n",
+      "gc: collectable <builtin_method 0x00000205D6B435B0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43600>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43420>\n",
+      "gc: collectable <builtin_method 0x00000205D6B436A0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B436F0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43650>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43790>\n",
+      "gc: collectable <builtin_method 0x00000205D6B437E0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43740>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43880>\n",
+      "gc: collectable <builtin_method 0x00000205D6B438D0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43830>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43920>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43970>\n",
+      "gc: collectable <builtin_method 0x00000205D6B439C0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43A10>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43A60>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43AB0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43B00>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43BA0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43BF0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43C40>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43C90>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43CE0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43D30>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43D80>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43B50>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43E20>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43E70>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43EC0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43F10>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43F60>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43FB0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50040>\n",
+      "gc: collectable <builtin_method 0x00000205D6B43DD0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B500E0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50130>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50090>\n",
+      "gc: collectable <builtin_method 0x00000205D6B501D0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50220>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50180>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50270>\n",
+      "gc: collectable <builtin_method 0x00000205D6B502C0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50310>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50360>\n",
+      "gc: collectable <builtin_method 0x00000205D6B503B0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50400>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50450>\n",
+      "gc: collectable <builtin_method 0x00000205D6B504A0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B504F0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50540>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50590>\n",
+      "gc: collectable <builtin_method 0x00000205D6B505E0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50630>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50860>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50810>\n",
+      "gc: collectable <function 0x00000205D6A63CE0>\n",
+      "gc: collectable <dict 0x00000205D6498A40>\n",
+      "gc: collectable <tuple 0x00000205D6B248C0>\n",
+      "gc: collectable <cell 0x00000205D65EC550>\n",
+      "gc: collectable <tuple 0x00000205D6B58800>\n",
+      "gc: collectable <tuple 0x00000205D6B587C0>\n",
+      "gc: collectable <tuple 0x00000205D6B672C0>\n",
+      "gc: collectable <list 0x00000205D6B67D80>\n",
+      "gc: collectable <tuple 0x00000205D6B628C0>\n",
+      "gc: collectable <list 0x00000205D6B62100>\n",
+      "gc: collectable <tuple 0x00000205D6B5B0C0>\n",
+      "gc: collectable <list 0x00000205D6B5AF80>\n",
+      "gc: collectable <tuple 0x00000205D6B5B000>\n",
+      "gc: collectable <tuple 0x00000205D6B58BC0>\n",
+      "gc: collectable <tuple 0x00000205D6B583C0>\n",
+      "gc: collectable <list 0x00000205D6B5A540>\n",
+      "gc: collectable <tuple 0x00000205D6B58A40>\n",
+      "gc: collectable <tuple 0x00000205D6B59C80>\n",
+      "gc: collectable <tuple 0x00000205D6B59640>\n",
+      "gc: collectable <tuple 0x00000205D6B59580>\n",
+      "gc: collectable <list 0x00000205D6B59280>\n",
+      "gc: collectable <tuple 0x00000205D6B62500>\n",
+      "gc: collectable <tuple 0x00000205D6B62480>\n",
+      "gc: collectable <tuple 0x00000205D6B62BC0>\n",
+      "gc: collectable <list 0x00000205D6B5A1C0>\n",
+      "gc: collectable <tuple 0x00000205D6B62B80>\n",
+      "gc: collectable <tuple 0x00000205D6B62B40>\n",
+      "gc: collectable <list 0x00000205D6B58B80>\n",
+      "gc: collectable <tuple 0x00000205D6B61780>\n",
+      "gc: collectable <tuple 0x00000205D6B619C0>\n",
+      "gc: collectable <list 0x00000205D6B5A340>\n",
+      "gc: collectable <tuple 0x00000205D6B61540>\n",
+      "gc: collectable <list 0x00000205D6B59E00>\n",
+      "gc: collectable <tuple 0x00000205D6B60FC0>\n",
+      "gc: collectable <tuple 0x00000205D6B60F00>\n",
+      "gc: collectable <tuple 0x00000205D6B60B00>\n",
+      "gc: collectable <tuple 0x00000205D6B60B40>\n",
+      "gc: collectable <tuple 0x00000205D6B61A80>\n",
+      "gc: collectable <list 0x00000205D6B5B280>\n",
+      "gc: collectable <tuple 0x00000205D6B61880>\n",
+      "gc: collectable <tuple 0x00000205D6B618C0>\n",
+      "gc: collectable <tuple 0x00000205D6B61680>\n",
+      "gc: collectable <tuple 0x00000205D6B61100>\n",
+      "gc: collectable <tuple 0x00000205D6B610C0>\n",
+      "gc: collectable <tuple 0x00000205D6B5A600>\n",
+      "gc: collectable <type 0x00000205D597C8E0>\n",
+      "gc: collectable <tuple 0x00000205D6B5AEC0>\n",
+      "gc: collectable <MyObject 0x00000205D65C6C60>\n",
+      "gc: collectable <MyObject 0x00000205D647D520>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50B80>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50E00>\n",
+      "gc: collectable <builtin_method 0x00000205D6B50F90>\n",
+      "gc: collectable <builtin_method 0x00000205D6B51940>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52840>\n",
+      "gc: collectable <builtin_method 0x00000205D6B527F0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52890>\n",
+      "gc: collectable <builtin_method 0x00000205D6B528E0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52930>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52980>\n",
+      "gc: collectable <builtin_method 0x00000205D6B529D0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52A20>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52A70>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52AC0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52B10>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52B60>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52BB0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52C00>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52C50>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52CA0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52CF0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52D40>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52D90>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52DE0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52E30>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52E80>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52ED0>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52F20>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52F70>\n",
+      "gc: collectable <builtin_method 0x00000205D6B52FC0>\n",
+      "gc: collectable <list 0x00000205D64A3D40>\n",
+      "gc: collectable <dict 0x00000205D6498FC0>\n",
+      "gc: collectable <function 0x00000205D6A63C40>\n",
+      "gc: collectable <function 0x00000205D6A637E0>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B60900>\n",
+      "gc: collectable <getset_descriptor 0x00000205D6B62F40>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "592"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Handled Circular reference\n",
+    "import gc\n",
+    "\n",
+    "class MyObject:\n",
+    "    def __init__(self, name):\n",
+    "        self.name = name\n",
+    "        print(f\"Object {self.name} created\")\n",
+    "\n",
+    "    def __del__(self):\n",
+    "        print(f\"Object {self.name} deleted\")\n",
+    "\n",
+    "# Create circular reference\n",
+    "obj1 = MyObject(\"obj1\")\n",
+    "obj2 = MyObject(\"obj2\")\n",
+    "obj1.ref = obj2\n",
+    "obj2.ref = obj1\n",
+    "\n",
+    "del obj1\n",
+    "del obj2\n",
+    "\n",
+    "## Manually trigger the garbage collection\n",
+    "gc.collect()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Generators For Memory Efficiency\n",
+    "#Generators allow you to produce items one at a time, using memory efficiently by only keeping one item in memory at a time.\n",
+    "\n",
+    "def generate_numbers(n):\n",
+    "    for i in range(n):\n",
+    "        yield i\n",
+    "\n",
+    "## using the generator\n",
+    "for num in generate_numbers(100000):\n",
+    "    print(num)\n",
+    "    if num>10:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Profiling Memory USage with tracemalloc\n",
+    "import tracemalloc\n",
+    "\n",
+    "def create_list():\n",
+    "    return [i for i in range(10000)]\n",
+    "\n",
+    "def main():\n",
+    "    tracemalloc.start()\n",
+    "    \n",
+    "    create_list()\n",
+    "    \n",
+    "    snapshot = tracemalloc.take_snapshot()\n",
+    "    top_stats = snapshot.statistics('lineno')\n",
+    "    \n",
+    "    print(\"[ Top 10 ]\")\n",
+    "    for stat in top_stats[::]:\n",
+    "        print(stat)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ Top 10 ]\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\selectors.py:314: size=144 KiB, count=3, average=48.0 KiB\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\compilerop.py:174: size=11.2 KiB, count=122, average=94 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\builtin_trap.py:70: size=6512 B, count=1, average=6512 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\json\\decoder.py:353: size=5812 B, count=92, average=63 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:272: size=3800 B, count=10, average=380 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\codeop.py:118: size=3509 B, count=47, average=75 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\zmq\\sugar\\attrsettr.py:45: size=3431 B, count=73, average=47 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\compilerop.py:86: size=3395 B, count=52, average=65 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\traitlets\\traitlets.py:731: size=2883 B, count=46, average=63 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\jsonutil.py:111: size=2050 B, count=41, average=50 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\traitlets\\traitlets.py:1543: size=2031 B, count=33, average=62 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\jsonutil.py:75: size=1456 B, count=7, average=208 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\re\\_compiler.py:759: size=1360 B, count=5, average=272 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:346: size=1328 B, count=16, average=83 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\traitlets\\traitlets.py:1514: size=1320 B, count=11, average=120 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\session.py:1057: size=1276 B, count=11, average=116 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\session.py:100: size=1243 B, count=8, average=155 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\zmq\\sugar\\socket.py:369: size=1152 B, count=5, average=230 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\zmq\\sugar\\socket.py:809: size=1056 B, count=6, average=176 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:568: size=864 B, count=16, average=54 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:917: size=800 B, count=20, average=40 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:276: size=776 B, count=8, average=97 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\compiler.py:91: size=726 B, count=7, average=104 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\asyncio\\events.py:84: size=648 B, count=6, average=108 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\_weakrefset.py:88: size=640 B, count=8, average=80 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:1349: size=640 B, count=4, average=160 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:287: size=632 B, count=7, average=90 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:154: size=568 B, count=1, average=568 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3104: size=520 B, count=4, average=130 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:1027: size=480 B, count=8, average=60 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\history.py:782: size=440 B, count=2, average=220 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:775: size=424 B, count=2, average=212 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\history.py:836: size=416 B, count=6, average=69 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\zmq\\sugar\\context.py:354: size=416 B, count=4, average=104 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\history.py:790: size=416 B, count=1, average=416 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\history.py:789: size=416 B, count=1, average=416 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\fnmatch.py:46: size=400 B, count=5, average=80 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\history.py:783: size=393 B, count=1, average=393 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3334: size=376 B, count=1, average=376 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:262: size=360 B, count=5, average=72 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:261: size=360 B, count=5, average=72 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\ipkernel.py:362: size=360 B, count=1, average=360 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:269: size=344 B, count=3, average=115 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\dateutil\\parser\\_parser.py:1195: size=336 B, count=7, average=48 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:912: size=320 B, count=8, average=40 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3566: size=320 B, count=5, average=64 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\asyncio\\base_events.py:792: size=312 B, count=3, average=104 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:527: size=304 B, count=4, average=76 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3517: size=296 B, count=1, average=296 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\tornado\\queues.py:248: size=288 B, count=2, average=144 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\asyncio\\base_events.py:427: size=288 B, count=2, average=144 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:142: size=288 B, count=1, average=288 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\fnmatch.py:185: size=280 B, count=5, average=56 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\fnmatch.py:52: size=280 B, count=5, average=56 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:534: size=264 B, count=1, average=264 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:435: size=248 B, count=1, average=248 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\iostream.py:121: size=248 B, count=1, average=248 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\threading.py:894: size=244 B, count=4, average=61 B\n",
+      "<frozen ntpath>:66: size=240 B, count=5, average=48 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\zmq\\sugar\\socket.py:806: size=240 B, count=3, average=80 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\logging\\__init__.py:1622: size=240 B, count=1, average=240 B\n",
+      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_37980\\3835544042.py:7: size=232 B, count=2, average=116 B\n",
+      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_37980\\3835544042.py:4: size=232 B, count=2, average=116 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:545: size=232 B, count=1, average=232 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\zmqshell.py:549: size=224 B, count=3, average=75 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\compilerop.py:171: size=216 B, count=3, average=72 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3223: size=208 B, count=4, average=52 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py:235: size=208 B, count=3, average=69 B\n",
+      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_37980\\811388706.py:22: size=208 B, count=2, average=104 B\n",
+      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_37980\\3581627739.py:22: size=208 B, count=2, average=104 B\n",
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+      "C:\\Users\\win10\\AppData\\Local\\Temp\\ipykernel_37980\\2547200138.py:22: size=208 B, count=2, average=104 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\session.py:1085: size=208 B, count=1, average=208 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:770: size=208 B, count=1, average=208 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\asyncio\\futures.py:418: size=200 B, count=5, average=40 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\session.py:688: size=194 B, count=2, average=97 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\jupyter_client\\session.py:750: size=192 B, count=2, average=96 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3224: size=176 B, count=4, average=44 B\n",
+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\ipykernel\\kernelbase.py:1182: size=173 B, count=2, average=86 B\n",
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+      "e:\\UDemy Final\\python\\venv\\Lib\\site-packages\\traitlets\\traitlets.py:1540: size=120 B, count=2, average=60 B\n",
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+     ]
+    }
+   ],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/16-Multithreading and Multiprocessing/advance_multi_processing.py b/16-Multithreading and Multiprocessing/advance_multi_processing.py
new file mode 100644
index 000000000..2544a09b9
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/advance_multi_processing.py	
@@ -0,0 +1,17 @@
+###  Multiprocessing with ProcessPoolExecutor
+
+from concurrent.futures import ProcessPoolExecutor
+import time
+
+def square_number(number):
+    time.sleep(2)
+    return f"Square: {number * number}"
+
+numbers=[1,2,3,4,5,6,7,8,9,11,2,3,12,14]
+if __name__=="__main__":
+
+    with ProcessPoolExecutor(max_workers=3) as executor:
+        results=executor.map(square_number,numbers)
+
+    for result in results:
+        print(result)
\ No newline at end of file
diff --git a/16-Multithreading and Multiprocessing/advance_multi_threading.py b/16-Multithreading and Multiprocessing/advance_multi_threading.py
new file mode 100644
index 000000000..89ca2eec3
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/advance_multi_threading.py	
@@ -0,0 +1,16 @@
+### Multithreading With Thread Pool Executor
+
+from concurrent.futures import ThreadPoolExecutor
+import time
+
+def print_number(number):
+    time.sleep(1)
+    return f"Number :{number}"
+
+numbers=[1,2,3,4,5,6,7,8,9,0,1,2,3]
+
+with ThreadPoolExecutor(max_workers=3) as executor:
+    results=executor.map(print_number,numbers)
+
+for result in results:
+    print(result)
\ No newline at end of file
diff --git a/16-Multithreading and Multiprocessing/factorial_multi_processing.py b/16-Multithreading and Multiprocessing/factorial_multi_processing.py
new file mode 100644
index 000000000..c4b1548d4
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/factorial_multi_processing.py	
@@ -0,0 +1,40 @@
+'''
+Real-World Example: Multiprocessing for CPU-bound Tasks
+Scenario: Factorial Calculation
+Factorial calculations, especially for large numbers, 
+involve significant computational work. Multiprocessing 
+can be used to distribute the workload across multiple 
+CPU cores, improving performance.
+
+'''
+
+import multiprocessing
+import math
+import sys
+import time
+
+# Increase the maximum number of digits for integer conversion
+sys.set_int_max_str_digits(100000)
+
+## function to compute factorials of a given number 
+
+def computer_factorial(number):
+    print(f"Computing factorial of {number}")
+    result=math.factorial(number)
+    print(f"Factorial of {number} is {result}")
+    return result
+
+if __name__=="__main__":
+    numbers=[5000,6000,700,8000]
+
+    start_time=time.time()
+
+    ##create a pool of worker processes
+    with multiprocessing.Pool() as pool:
+        results=pool.map(computer_factorial,numbers)
+
+    end_time=time.time()
+
+    print(f"Results: {results}")
+    print(f"Time taken: {end_time - start_time} seconds")
+
diff --git a/16-Multithreading and Multiprocessing/multi_processing.py b/16-Multithreading and Multiprocessing/multi_processing.py
new file mode 100644
index 000000000..70f4957a2
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/multi_processing.py	
@@ -0,0 +1,35 @@
+## PRocesses that run in parallel
+### CPU-Bound Tasks-Tasks that are heavy on CPU usage (e.g., mathematical computations, data processing).
+## PArallel execution- Multiple cores of the CPU
+
+import multiprocessing
+
+import time
+
+def square_numbers():
+    for i in range(5):
+        time.sleep(1)
+        print(f"Square: {i*i}")
+
+def cube_numbers():
+    for i in range(5):
+        time.sleep(1.5)
+        print(f"Cube: {i * i * i}")
+
+if __name__=="__main__":
+
+    ## create 2 processes
+    p1=multiprocessing.Process(target=square_numbers)
+    p2=multiprocessing.Process(target=cube_numbers)
+    t=time.time()
+
+    ## start the process
+    p1.start()
+    p2.start()
+
+    ## Wait for the process to complete
+    p1.join()
+    p2.join()
+
+    finished_time=time.time()-t
+    print(finished_time)
\ No newline at end of file
diff --git a/16-Multithreading and Multiprocessing/multi_threading.py b/16-Multithreading and Multiprocessing/multi_threading.py
new file mode 100644
index 000000000..4f2ca6550
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/multi_threading.py	
@@ -0,0 +1,33 @@
+### Multithreading
+## When to use Multi Threading
+###I/O-bound tasks: Tasks that spend more time waiting for I/O operations (e.g., file operations, network requests).
+###  Concurrent execution: When you want to improve the throughput of your application by performing multiple operations concurrently.
+
+import threading
+import time
+
+def print_numbers():
+    for i in range(5):
+        time.sleep(2)
+        print(f"Number:{i}")
+
+def print_letter():
+    for letter in "abcde":
+        time.sleep(2)
+        print(f"Letter: {letter}")
+
+##create 2 threads
+t1=threading.Thread(target=print_numbers)
+t2=threading.Thread(target=print_letter)
+
+t=time.time()
+## start the thread
+t1.start()
+t2.start()
+
+### Wait for the threads to complete
+t1.join()
+t2.join()
+
+finished_time=time.time()-t
+print(finished_time)
\ No newline at end of file
diff --git a/16-Multithreading and Multiprocessing/webscrapping_multi_threading.py b/16-Multithreading and Multiprocessing/webscrapping_multi_threading.py
new file mode 100644
index 000000000..06eca14bc
--- /dev/null
+++ b/16-Multithreading and Multiprocessing/webscrapping_multi_threading.py	
@@ -0,0 +1,48 @@
+'''
+Real-World Example: Multithreading for I/O-bound Tasks
+Scenario: Web Scraping
+Web scraping often involves making numerous network requests to 
+fetch web pages. These tasks are I/O-bound because they spend a lot of
+time waiting for responses from servers. Multithreading can significantly
+improve the performance by allowing multiple web pages to be fetched concurrently.
+
+'''
+
+'''
+
+https://python.langchain.com/v0.2/docs/introduction/
+
+https://python.langchain.com/v0.2/docs/concepts/
+
+https://python.langchain.com/v0.2/docs/tutorials/
+'''
+
+import threading
+import requests
+from bs4 import BeautifulSoup
+
+urls=[
+'https://python.langchain.com/v0.2/docs/introduction/',
+
+'https://python.langchain.com/v0.2/docs/concepts/',
+
+'https://python.langchain.com/v0.2/docs/tutorials/'
+
+]
+
+def fetch_content(url):
+    response=requests.get(url)
+    soup=BeautifulSoup(response.content,'html.parser')
+    print(f'Fetched {len(soup.text)} characters from {url}')
+
+threads=[]
+
+for url in urls:
+    thread=threading.Thread(target=fetch_content,args=(url,))
+    threads.append(thread)
+    thread.start()
+
+for thread in threads:
+    thread.join()
+
+print("All web pages fetched")
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 24ca97ad3..07b0ec227 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,9 @@
 ipykernel
-numpy
\ No newline at end of file
+numpy
+pandas
+matplotlib
+seaborn
+flask
+memory_profiler
+streamlit
+scikit-learn