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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPrQRxJ64sfNOlST1PgkmyT"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"yCeK--Ix3Sa8"},"outputs":[],"source":[]},{"cell_type":"markdown","source":["# 1. Introduction\n","\n","This project focuses on data preprocessing and exploratory data analysis using Python for AI and Data Science applications.\n","The main objective of this project is to clean, understand, and analyze the dataset so that it can be effectively used for AI and Data Science tasks."],"metadata":{"id":"P1ZwYe_w354R"}},{"cell_type":"markdown","source":["# 2. Dataset Description\n","\n","In this project, the Titanic dataset is used for data preprocessing and analysis.\n","The dataset contains information about passengers such as age, gender, passenger class, fare, and survival status.\n","It is a well-known dataset widely used in AI and Data Science for learning data preprocessing and exploratory data analysis."],"metadata":{"id":"i7hCMeZR4xxC"}},{"cell_type":"code","source":["# Load Pandas library\n","import pandas as pd\n","\n","# Read Titanic dataset\n","df = pd.read_csv(\"https://raw.githubusercontent.com/YBIFoundation/Dataset/main/Titanic.csv\")\n","df.head()   # Display first 5 rows"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":539},"id":"aeZtv6yO6AIl","executionInfo":{"status":"ok","timestamp":1766203878337,"user_tz":-330,"elapsed":485,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"11209b4a-9b80-456f-aa20-b2d13e359f15"},"execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   pclass  survived                                             name     sex  \\\n","0       1         1                    Allen, Miss. Elisabeth Walton  female   \n","1       1         1                   Allison, Master. Hudson Trevor    male   \n","2       1         0                     Allison, Miss. Helen Loraine  female   \n","3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n","4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n","\n","     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n","0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n","1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n","2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n","3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n","4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n","\n","                         home.dest  \n","0                     St Louis, MO  \n","1  Montreal, PQ / Chesterville, ON  \n","2  Montreal, PQ / Chesterville, ON  \n","3  Montreal, PQ / Chesterville, ON  \n","4  Montreal, PQ / Chesterville, ON  "],"text/html":["\n","  <div id=\"df-d056ac93-d49c-455c-b41a-75d867021119\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>pclass</th>\n","      <th>survived</th>\n","      <th>name</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>ticket</th>\n","      <th>fare</th>\n","      <th>cabin</th>\n","      <th>embarked</th>\n","      <th>boat</th>\n","      <th>body</th>\n","      <th>home.dest</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Allen, Miss. 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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 929,\n        \"samples\": [\n          \"371060\",\n          \"330971\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.758668239174135,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 281,\n        \"samples\": [\n          5.0,\n          9.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 186,\n        \"samples\": [\n          \"C90\",\n          \"C80\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"boat\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"B\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97.6969219960031,\n        \"min\": 1.0,\n        \"max\": 328.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          297.0,\n          19.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"home.dest\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 369,\n        \"samples\": [\n          \"Co Limerick, Ireland Sherbrooke, PQ\",\n          \"Amenia, ND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":1}]},{"cell_type":"code","source":["# Check missing values in dataset\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":523},"id":"8vzC6Ta_9JHK","executionInfo":{"status":"ok","timestamp":1766203904069,"user_tz":-330,"elapsed":67,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"20327dd6-28d4-405e-b9f6-80d18d0b20b2"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pclass          0\n","survived        0\n","name            0\n","sex             0\n","age           263\n","sibsp           0\n","parch           0\n","ticket          0\n","fare            1\n","cabin        1014\n","embarked        2\n","boat          823\n","body         1188\n","home.dest     564\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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<th>embarked</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>boat</th>\n","      <td>823</td>\n","    </tr>\n","    <tr>\n","      <th>body</th>\n","      <td>1188</td>\n","    </tr>\n","    <tr>\n","      <th>home.dest</th>\n","      <td>564</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Histogram for age\n","plt.figure(figsize=(8,5))\n","sns.histplot(df['age'], bins=30, kde=True, color='skyblue')\n","plt.xlabel(\"age\")\n","plt.ylabel(\"Count\")\n","plt.title(\"age Distribution\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":166},"id":"I6Bb2Hkw_eV5","executionInfo":{"status":"ok","timestamp":1766204638383,"user_tz":-330,"elapsed":412,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"6eb84d57-f282-4397-8b61-9c4896ce191d"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 1. Fill missing values in 'age' with the average (mean) age\n","df['age'] = df['age'].fillna(df['age'].mean())\n","\n","# 2. Fill the 1 missing value in 'fare' with the median value\n","df['fare'] = df['fare'].fillna(df['fare'].median())\n","\n","# 3. Drop the 'cabin' column because it has too many missing values (1014)\n","df.drop(columns=['cabin'], inplace=True)\n","\n","print(\"Missing values have been handled successfully!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Im5ZKst2Cr4H","executionInfo":{"status":"ok","timestamp":1766205989715,"user_tz":-330,"elapsed":38,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"1e532e47-d55a-4676-862a-fbb4bfca918d"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Missing values have been handled successfully!\n"]}]},{"cell_type":"code","source":["# Check the missing value count again\n","print(df.isnull().sum())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HKoPdHmXFL4M","executionInfo":{"status":"ok","timestamp":1766206018833,"user_tz":-330,"elapsed":36,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"48662408-c86a-4b10-a687-f0f2b2f8c164"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["pclass          0\n","survived        0\n","name            0\n","sex             0\n","age             0\n","sibsp           0\n","parch           0\n","ticket          0\n","fare            0\n","embarked        2\n","boat          823\n","body         1188\n","home.dest     564\n","dtype: int64\n"]}]},{"cell_type":"code","source":["# Create a bar chart for passenger classes using Pandas\n","df['pclass'].value_counts().plot(kind='bar', color='orange')\n","plt.title('Passenger Distribution by Class')\n","plt.xlabel('Class (1st, 2nd, 3rd)')\n","plt.ylabel('Number of Passengers')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":190},"id":"uIK_wMuYFR-Z","executionInfo":{"status":"ok","timestamp":1766206037046,"user_tz":-330,"elapsed":197,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"d5d082d9-fd47-4844-89c5-a2eed00dd33d"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df['age'] = df['age'].fillna(df['age'].mean())"],"metadata":{"id":"Jjvyg57tGqJW","executionInfo":{"status":"ok","timestamp":1766206390042,"user_tz":-330,"elapsed":24,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["df['fare'] = df['fare'].fillna(df['fare'].median())"],"metadata":{"id":"_y2pJ7F1GtC9","executionInfo":{"status":"ok","timestamp":1766206407407,"user_tz":-330,"elapsed":13,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["# 1. Fill missing 'age' values with the mean (average)\n","df['age'] = df['age'].fillna(df['age'].mean())\n","\n","# 2. Fill missing 'fare' values with the median\n","df['fare'] = df['fare'].fillna(df['fare'].median())\n","\n","# 3. Drop the 'cabin' column because it has too many missing values\n","# Added a check to avoid errors if the column is already deleted\n","if 'cabin' in df.columns:\n","    df.drop(columns=['cabin'], inplace=True)\n","\n","# 4. Verify that there are no more missing values\n","print(df.isnull().sum())\n","print(\"Data cleaning complete!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xFHVn8ENHZ6R","executionInfo":{"status":"ok","timestamp":1766206581670,"user_tz":-330,"elapsed":49,"user":{"displayName":"Akanksha Patil","userId":"00440784023467706562"}},"outputId":"7e8e3dad-3143-40ce-bfdc-5c798387b559"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["pclass          0\n","survived        0\n","name            0\n","sex             0\n","age             0\n","sibsp           0\n","parch           0\n","ticket          0\n","fare            0\n","embarked        2\n","boat          823\n","body         1188\n","home.dest     564\n","dtype: int64\n","Data cleaning complete!\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"-_AIB7WDHcIs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Conclusion\n","\n","I loaded the Titanic dataset, checked and visualized missing values, and filled them using mean (Age) and mode (Embarked). The dataset is now clean and ready for analysis."],"metadata":{"id":"NR1CI5zxKQs4"}}]}
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