Money_Laundering_Notebook_v2.ipynb

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      "source": [
        "%pip install --upgrade pip\n",
        "%pip install scikit-learn\n",
        "%pip install seaborn\n",
        "%pip install plotly\n",
        "%pip install tensorflow\n",
        "%pip install pandas\n",
        "%pip install pyodbc\n",
        "%pip install openpyxl\n",
        "%pip install nbformat\n",
        "%pip install dask"
      ]
    },
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      "source": [
        "import warnings\n",
        "import numpy as np # linear algebra breakdown\n",
        "import pandas as pd # data processing, CSV files input/output\n",
        "import matplotlib.pyplot as plt # graph plotting\n",
        "import seaborn as sns \n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import iplot\n",
        "\n",
        "from numpy import percentile\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from scipy import stats\n",
        "from scipy.stats import trim_mean\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import average_precision_score\n",
        "\n",
        "from dask import dataframe as dd\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
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      "source": [
        "    \"#url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158025&authkey=ACHHRI10a-phyio&em=2'\\n\",\n",
        "    \"url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158163&authkey=AFMTDEy7Ff3oCL8'\\n\",\n",
        "    \"#url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158165&authkey=APswAhpzUuOs4KQ'\\n\","
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      "source": [
        "import pandas as pd\n",
        "\n",
        "#Data Source Four - 16GB\n",
        "url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4a3b8562a2cb78b3%21158165&authkey=APswAhpzUuOs4KQ'\n",
        "\n",
        "#Data Source Three - 16GB\n",
        "#url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158164&authkey=AH8S2jQUTGfimec'\n",
        "\n",
        "#Data Source Two - 620MB\n",
        "#url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158163&authkey=AFMTDEy7Ff3oCL8'\n",
        "\n",
        "#Data Source One - 92MB\n",
        "#url ='https://onedrive.live.com/download?cid=4A3B8562A2CB78B3&resid=4A3B8562A2CB78B3%21158024&authkey=AO02aWctkJGh0Nc'\n",
        "\n",
        "df = pd.read_csv(url)\n",
        "df.info()"
      ]
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      "outputs": [],
      "source": [
        "df.head()\n",
        "df.tail()"
      ]
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      "outputs": [],
      "source": [
        "df.shape"
      ]
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      "source": [
        "print(\"Shape of dataset:\", df.shape)\n",
        "print(\"Overview of the data:\")\n",
        "print(df.head())\n"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "df['Is Laundering'] = df['Is Laundering'].replace({'Yes': 1, 'No':0})"
      ]
    },
    {
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      "source": [
        "df_subset = df"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "df_subset.shape"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "print(\"Description of the dataset:\")\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "# Scans the data to search the percentages of fraud vs no fraud\n",
        "amount = df.groupby('Is Laundering')['Is Laundering'].sum()\n",
        "fraud, unfraud = len(df[df['Is Laundering'] == 1]), len(df[df['Is Laundering'] == 0])\n",
        "fraud_perc, unfraud_perc = (fraud/len(df)) * 100, (unfraud/len(df))*100\n",
        "\n",
        "Loss = pd.DataFrame({'Fraud' : ['Fraud', 'No Fraud'], 'Total Amount' : [amount[1], amount[0]], 'Freq.' : [fraud, unfraud], '% perc.' : [fraud_perc, unfraud_perc]})\n",
        "\n",
        "Loss = Loss.set_index('Fraud')\n",
        "Loss"
      ]
    },
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      "outputs": [],
      "source": [
        "# Initalise the lists to store catagorical and numerical features\n",
        "catfeat = []\n",
        "numfeat = []\n",
        "\n",
        "for i in df.columns:\n",
        "    if(df[i].dtypes == 'Is Laundering'): catfeat.append(i)\n",
        "    else:\n",
        "        numfeat.append(i)\n",
        "print(f'The number of Objects Features : {len(catfeat)}')\n",
        "print(f'The number of Numerical Features : {len(numfeat)}')"
      ]
    },
    {
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      },
      "outputs": [],
      "source": [
        "print(f'Number of missing values : {df.isnull().sum().sum()}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [],
      "source": [
        "# This scans the data for any duplicates that are within the datasets\n",
        "namedfeat = ['Amount Received', 'Amount Paid', 'Payment Format']\n",
        "for i in df[namedfeat]:\n",
        "    if(df[i].duplicated().sum() > 0): print(f'{i} has {df[i].duplicated().sum()} duplicates') \n",
        "\n",
        "df[namedfeat].describe().T"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "# Checks the quality of the withdrawal amounts in the dataset\n",
        "class_counts_with = df['Amount Received'].value_counts()\n",
        "class_counts_percentage_with = df['Amount Received'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Checks the quality of the deposit amounts in the dataset\n",
        "class_counts_dep = df['Amount Paid'].value_counts()\n",
        "class_counts_percentage_dep = df['Amount Paid'].value_counts(normalize=True) * 100\n",
        "\n",
        "print(\"Amount Received: \\n\", class_counts_with)\n",
        "print(\"Amount Paid: \\n\", class_counts_dep)"
      ]
    },
    {
      "cell_type": "code",
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      },
      "outputs": [],
      "source": [
        "# Checks the quality of the deposit amounts in the dataset\n",
        "class_counts_lau = df['Is Laundering'].value_counts()\n",
        "class_counts_percentage_lau = df['Is Laundering'].value_counts(normalize=True) * 100\n",
        "\n",
        "print(\"Is Laundering: \\n\", class_counts_lau)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      },
      "outputs": [],
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "px.scatter(df, x ='Amount Received', y ='Amount Paid', color ='Payment Format')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "nteract": {
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      },
      "outputs": [],
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "px.scatter(df, x ='Payment Format', y ='Amount Paid', color ='Is Laundering')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      },
      "outputs": [],
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "px.scatter_3d(df, x ='Amount Received', y ='Amount Paid', z='Payment Format', color ='Payment Currency')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Read data from csv file\n",
        "data = pd.read_excel(url)\n",
        "\n",
        "# Data to plot\n",
        "labels = data['Payment Format']\n",
        "values = data['Is Laundering']\n",
        "\n",
        "# Plot\n",
        "plt.bar(labels, values)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
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      },
      "outputs": [],
      "source": [
        "df_subset['Amount Paid'] = pd.to_numeric(df_subset['Amount Paid'], errors='coerce')\n",
        "df_subset.boxplot(column='Amount Paid', by='Is Laundering')\n",
        "\n",
        "plt.title('Box plot graph for Payments vs Fraud')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Read data from csv file\n",
        "data = pd.read_excel(url)\n",
        "\n",
        "# Split the dataset\n",
        "X = df['Is Laundering']\n",
        "y = df['Payment Format']\n",
        "\n",
        "# Plot\n",
        "bars = plt.bar(X, y)\n",
        "plt.bar_label(bars)\n",
        "\n",
        "plt.show()"
      ]
    }
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