Titanic Practice 1.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "pd.options.mode.chained_assignment = None\n",
    "import joblib"
   ]
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Read the Titanic_train.csv file here"
   ]
  },
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   "source": [
    "For this example, we are only extracting 2 things: Class and sex. \n",
    "\n",
    "Do that below"
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   "source": [
    "# Extract the pclass and sex into a new Dataframe"
   ]
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   "outputs": [],
   "source": [
    "# Convert pclass to pure numbers"
   ]
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the sex with 0 for female, 1 for male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the expected result dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create test/train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the random forest instance, and train it with training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the accuracy of your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the model to a file called \"titanic_model2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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